What is a Tax-Deferred Account?

Investors have several options when it comes to planning for their retirement. They can make decisions based on several factors. Among other things, investors must consider the tax aspects of their retirement plans. It is a part of both their tax planning and retirement planning process. One of the accounts which investors can invest in includes tax-deferred accounts.

What is a Tax-Deferred Account?

A tax-deferred account is an investment account that allows investors to defer their taxes until withdrawal. With this account, investors can postpone the taxes they pay on any money they invest into it. Therefore, they don't have to account for the tax implications of these accounts until they withdraw the money. Usually, this occurs after retiring. Thus, investors can effectively postpone their taxes until after retirement.

Investors that invest in tax-deferred accounts don't have to account for the returns they generate on these accounts. It also means that they don't have to report the income on their tax returns. However, as soon as they withdraw the amount from their tax-deferred account, it becomes taxable. Tax-deferred accounts are sensible investment decisions if investors are in a high tax bracket at the time of investment.

How does a Tax-Deferred Account work?

Tax-deferred accounts work similarly to other investments. Individuals can choose their preferred accounts, which generate returns after a specific time. Unlike other investments, however, these returns are not taxable when the individual earns them. Therefore, they don't have to account for the tax implications when they get the returns.

Instead, these returns become taxable when the individual withdraws the amount. There are some rules which may restrict investors from withdrawing based on the type of account they have. Some accounts may also come with early withdrawal penalties. However, once investors do withdraw the amount, they must pay the due taxes.

Therefore, investors can postpone their taxes on these returns for as long as possible. These accounts allow investors to benefit and can be crucial for tax planning purposes. On top of that, investors can invest in various tax-deferred accounts to help them maximize their benefits from postponing their taxes. For some tax-deferred accounts, the savings that investors make can be substantial.

What are the types of Tax-Deferred Accounts?

Investors have many options when it comes to investing in tax-deferred accounts. The most common options preferred by investors are 401(k)s and individual retirement accounts (IRA). Both have further types, such as traditional or Roth 401(k)s and IRAs. In countries such as Canada, these may include registered retirement savings plans (RRSP).

Apart from these, investors also have other options. These include whole life insurance, I Bonds or EE Bonds, fixed deferred annuities, variable annuities, etc. When choosing between these, investors need to consider their needs. Most importantly, however, they must ponder the benefits they can get from each of these.

Conclusion

Tax-deferred accounts are a great investment for investors looking for retirement plans with tax planning benefits. These accounts allow investors to postpone their returns. Therefore, they don't have to pay taxes on any returns they generate on these accounts. However, once they withdraw the amount from the account, they must pay taxes.

Originally Published Here: What is a Tax-Deferred Account?

What Are the Correct Methods for Evaluating a Trading Strategy?

The trading literature mostly deals with the design and development of trading strategies. There is very little discussion on the topic of how to properly validate them, and most of the system validation techniques have been developed under a traditional statistical framework. Article [1], however, argued that using a traditional single testing framework is not enough, and provided some guidance on how to properly evaluate trading strategies using a multiple testing framework. It concluded,

In summary, the message of our research is simple. Researchers in finance, whether practitioners or academics, need to realize that they will find seemingly successful trading strategies by chance. We can no longer use the traditional tools of statistical analysis that assume that no one has looked at the data before and there is only a single strategy tried. A multiple testing framework offers help in reducing the number of false strategies adapted by firms. Two sigma is no longer an appropriate benchmark for evaluating trading strategies.

We agree that evaluating a trading strategy under a single testing framework is not enough, as the strategy might happen to be a winner by chance. However, we believe that the approach proposed by the authors is not practical, i.e. it’s not scalable, intuitive, and cannot be easily implemented.

The article then pointed out that the Sharpe ratio is not an appropriate measure of risk-adjusted return. This is true, especially with options trading strategies whose PnLs are not normally distributed and are often heavily skewed.

Our work has two important limitations. First, for a number of applications the Sharpe Ratio is not appropriate because the distribution of the strategy returns is not Normal. For example, two trading strategies might have identical Sharpe Ratios but one of them might be preferred because it has less severe downside risk.…

The article also discussed the importance of adding a low-correlated trading strategy as a diversifier to a portfolio.

…our work focuses on individual strategies. In actual practice, the investment manager needs to examine how the proposed strategy interacts with the current collection of strategies. For example, a strategy with a lower Sharpe might be preferred because the strategy is relatively uncorrelated with current strategies. The denominator in the Sharpe Ratio is simply the strategy volatility and does not measure the contribution of the strategy to the portfolio volatility. The strategy portfolio problem, i.e. adding a new strategy to a portfolio of existing strategies is the topic of Harvey and Liu (2014c).

For more discussion on the benefit of adding a diversifier to a portfolio, read Impact of a Low Correlation Trading Strategy

References

[1] Harvey, Campbell R. and Liu, Yan, Evaluating Trading Strategies, https://ssrn.com/abstract=2474755, 2014

Article Source Here: What Are the Correct Methods for Evaluating a Trading Strategy?

Risk-Adjusted Measures of Performance

When investors make investment choices, they consider various factors. However, the most crucial among those is the risks and rewards they can get from those choices. Usually, investors expect the highest returns possible from their investments. However, each investment has a risk associated with it that can be detrimental to investors. Therefore, they may need to adjust their rewards for the risks they are willing to take.

What are Risk-Adjusted Measures of Performance?

Risk-adjusted measures of performance are metrics that can help investors adjust for the risks they take for their investments. These allow investors to consider both the risks and rewards they can get on their investments instead of focusing on returns only. Usually, the higher the risk is, the higher the returns are as well. By using risk-adjusted measures of performance, investors can compare various investments with varying risk levels.

There are several risk-adjusted measures of performance that investors can use. Among those, the most crucial is the risk-adjusted return. On top of that, investors can also use various ratios to compare investments that come with varying risk levels.

What is the Risk-Adjusted Return?

The risk-adjusted return represents the return that investors can get from an investment after accounting for the risks associated with it. As mentioned above, this metric allows investors to calculate the relative profits they can gain from their investments. By considering the risks associated with the investment, investors can get better insights into the rewards they can achieve.

The risk-adjusted return allows investors to compare various investments with varying risks. It works on a simple premise, allowing investors to choose investments with the highest returns for a given risk level. For example, if two investments offered the same returns, the risk-adjusted return will be higher for the ones with the lower risks.

What are some Risk-Adjusted Measures of Performance ratios?

Investors can get the risk-adjusted performance for an investment as ratios. It makes the comparison process more manageable, allowing investors to make better decisions. There are several ratios that can help investors gauge an investment's risk-adjusted performance measure. However, the most commonly used ones include the Sharpe and Treynor ratios.

Sharpe Ratio

The Sharpe Ratio allows investors to measure the returns they get from an investment that exceeds the risk-free rate per unit of standard deviation. Investors can calculate it by calculating an investment's market risk premium. After that, they must divide it by the investment's standard deviation to get the Sharpe ratio. Usually, investors prefer a high Sharpe ratio, which signifies the highest returns for the given standard deviation.

Treynor Ratio

Another ratio commonly used for risk-adjusted performance measures is the Treynor ratio. It uses the same principle as the Sharpe ratio. Instead of considering an investment's standard deviation, however, the Treynor ratio uses its beta. However, the ratio's principle remains the same. The higher the Treynor ratio is, the higher the risk-adjusted return for the investment will be.

Conclusion

Risk-adjusted measures of performance allow investors to calculate the returns they get from their investments after accounting for the risks. Therefore, these allow investors to compare investments based on how much risk they are willing to accept. There are several metrics that can help investors in calculating the risk-adjusted performance for investments.

Originally Published Here: Risk-Adjusted Measures of Performance

Home Bias in Equity Portfolios

When investors choose a portfolio of stocks or securities to invest in, they have several options. They can choose between both local and international markets to select their investments. It gives them great flexibility when it comes to building a diversified portfolio. Sometimes, however, investors may allow bias to influence their decisions when selecting the market where they place their investments.

What is Home Bias?

The term home bias represents investors' tendency to prefer domestic investments. In this process, they ignore the certainty they can get from diversifying into foreign markets. There are some specific types of investors that demonstrate this bias compared to others. Usually, these investors have faced limitations when it comes to investing in foreign markets. Therefore, they base their decision to invest locally on those experiences.

In the present, investing in foreign markets is seamless. There are no significant limitations that investors may face. However, some investors still allow their bias towards local markets to influence their decisions. Investors are also more likely to choose local investments as they may worry about the prospect of entering unknown markets. Either way, they lose the opportunity to increase their returns and build a diversified portfolio of investments.

Home bias in equity portfolios can also exist when investors prefer local equities compared to foreign ones. This bias exists for investors that are active in the stock market. Even when investors invest in foreign markets, their portfolios comprise a substantial portion of local equities. It also constitutes home bias in equity portfolios.

How does Home Bias work?

Having a diversified portfolio of investments is crucial for investors. It means they must include different types of investments in their portfolios. These may consist of stocks, debt instruments, real estate, commodities, etc. On top of that, they must not concentrate all their investments in the same market. Due to the possibilities available, investors can build a diversified portfolio by investing in local and foreign markets.

However, some investors may not believe in investing in foreign markets. As mentioned above, this bias may come from their fear of entering unknown markets or previous experiences. Due to this, investors may focus all their investments on their local markets. This bias increases their risks associated with having a portfolio that focuses on a specific market. On top of that, investors also lose the opportunity to increase their returns.

Why is Home Bias important?

Home bias is crucial for investors as it forces them to concentrate on a local market. Due to this, they face many risks associated with having an undiversified portfolio. As mentioned, some investors may also be active in foreign markets. Due to home bias, however, they may still dedicate a large portion of their portfolio to local investments.

Identifying and avoiding home bias is also crucial for investors. Any investor active in the local markets can easily avoid this bias by considering both local and foreign investing options. Home bias can also cause investors to neglect better investment opportunities. In some cases, however, this bias can also be beneficial for investors with the returns they can get on local investments.

Conclusion

Home bias influences investors to concentrate their investments in local markets. By doing so, they neglect the investment opportunities available in foreign markets. This bias may arise from investors’ past experiences or fear of entering unknown markets. Home bias can lead to lost returns and undiversified portfolios.

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Herding Behaviour in Stock Market

What is Herding Behavior in Stock Market?

Herding behaviour in the stock market is when investors make decisions based on other investors' investments. Other names for this behavior include herd mentality and herd instinct. Herding behavior explains investors' tendency to imitate or follow what other investors are doing. Usually, they believe that what other investors are doing will be profitable without any analysis.

Herding behaviour in the stock market occurs due to several factors. These may include investors' emotions or their instinct. Without carrying any independent analysis of their own, investors may prefer imitating other investors, expecting to succeed. This phenomenon stems from behavioural finance, where it can lead to herd bias.

Herding behaviour bias (or simply, herd bias) is when investors base their decisions on other investors' investments. It is a naturally occurring behaviour in humans in all fields. Herding behaviour bias leads to investment bubbles, such as the one created by Bitcoin recently. In short, herding behaviour is the tendency of individuals to confirm the actions of a larger group.

How does the Herding Behavior impact investors?

Investors involved in herding behaviour may experience losses. According to a report, herding bias is the highest loss-making behavioural bias for investors. Therefore, it places herding on the top of any investor's avoidance list. The herding behaviour can impact investors in one of two ways. It either exposes investors to volatility or creates a bubble in the market.

Firstly, the herding mentality can cause a series of over- or under-reactions in the market. Due to the large extent of these reactions, the market suffers from volatility. On the other hand, it also creates an asset bubble. Usually, everyone in the herd assumes their decisions are rational. This herd action creates a sudden rise in the asset's price and also causes it to fall in the future. This way, it causes investors to suffer from the creation of volatility and bubbles.

Why do people suffer from Herding Behavior Bias?

The primary reason why individuals or investors become a part of a herd is because of their nature. Humans have long been part of the groups to survive and evolve. Similarly, humans are social animals which tempts them to be a part of the group. Therefore, their instincts entice them to follow what others are doing.

Another reason for the herding behaviour is the lack of information. When investors don't analyze information on their own, they become dependent on others' decisions. Once they become a part of a group, they assume the herd has accurate knowledge to manage their investments. In most cases, the opposite applies.

How to avoid Herding Behavior?

In theory, it is straightforward to avoid herding behaviour. By not following what others are doing, investors can easily avoid the effects of herding behaviour. In practice, however, it is not as easy. The best way to protect against herding behaviour is to understand how it works. For example, when buying a company's stock, investors can look at how the market perceives it.

Avoiding herding behaviour does not mean that investors must not invest in securities where others are investing. Instead, they must perform an independent analysis of any investment they make. This way, they can ensure they don't become a part of a herd. Even if they do, they will realize when they should leave it and not suffer losses.

Conclusion

Herding behaviour in the stock market occurs when investors base their decisions on what others are doing. Usually, they don't collect any information and assume the herd has accurate information regarding investments. It leads to herding behaviour bias and can cause bubbles and volatility to create. Despite that, herding behaviour is easily avoidable.

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Cointegration or Correlation, Which Method is Better for Pair Trading?

Pair trading or statistical arbitrage is a popular quantitative trading strategy. Basically, we choose a pair of assets for trading, and when the assets’ prices diverge, we bet on them reverting back to the mean. The assets are often stocks, but they can be anything, ranging from commodities, interest rate products, to exchange-traded funds.

To choose a pair for trading, we often make a decision based upon,

• Correlation of assets,
• Cointegration of the underlyings, or
• Other methods.

A common question we often ask ourselves is which method is superior. We used to believe that using correlation or cointegration does not matter, as long as the pair mean reverts. However, article [1] shed some light on the matter. After conducting research, the authors concluded that choosing pairs based on cointegration resulted in superior returns,

Our results confirm the weak excess returns observed in the recent publications for the minimum distance method. The trades initiated following the stationarity of the price ratio are not able to generate, after transaction costs, large and significant excess returns. The key empirical fact revealed by this study concerns cointegration. After controlling for risk factors, transaction costs and data-snooping biases, cointegration-based pairs trading exhibits high and robust positive alpha. During a period of more than 10 years, even the least profitable parameterization dealing with cointegration delivers excess returns greater than 1.38% per month. Returns can rise up to 5% per month. Cointegration reduces significantly nonconvergence risk.

The article stated that cointegration reduced divergence risks, but did not explain why. This is an area for further research. Additionally, it also pointed out,

If pairs trading returns are not related to the equity premium, does the volatility/Vix index matter?  Do and Faff (2010) regress pairs returns against market volatility over the same month but did not find any significant effect. A new direction could analyse the influence of conditioning openings to a certain level of volatility/Vix.

Based on our experience, we used to think that pair trading is implicitly shorting volatility, but as the article pointed out, this might not be the case. We look forward to new research results to learn more as to why this is not the case.

References

[1] N. Huck and K. Afawubo, Pairs trading and selection methods: is cointegration superior?, Applied Economics, 47:6, 599-613, 2015

Post Source Here: Cointegration or Correlation, Which Method is Better for Pair Trading?

What Is the Gamblers’ Fallacy?

What is the Gambler’s Fallacy?

The term gambler's fallacy refers to the belief that individuals have that the probability of a random event occurring in the future depends on the previous instance of that type of event. Another name used for the gambler's fallacy is the Monte Carlo fallacy. It occurs when individuals believe that the outcome of a single or several historical events influences the consequence of another event in the future.

When influenced by the gambler's fallacy, individuals tend to put overreliance on the outcome of historical events. They believe that if an event has occurred frequently in the past, it will happen again in the future. However, these outcomes are usually random and not influenced by historical events. As the name suggests, the gambler's fallacy is prevalent among gamblers.

How does the Gambler’s Fallacy work?

In theory, for events that have a random outcome, any historical results should not be influential. Hence, making predictions based on historical results is futile. Despite that, individuals may try to establish patterns even when none exist. Based on this, they end up making decisions although they understand the randomness involved with the event.

The gambler's fallacy stems from the misjudgment that investors make on a random event's outcome. Usually, the belief that random events are not truly random and may demonstrate some patterns or trends. Mostly, they use short-term information to establish this belief. In the long-term, however, investors suffer from losses due to basing their decisions on wrong opinions.

How can the Gambler’s Fallacy affect decision-making?

The gambler's fallacy tempts individuals to establish patterns and trends for random events. Therefore, it leads to wrong decisions made by these individuals. On top of that, by discovering such non-existent trends, individuals are likely to ignore any information opposing their beliefs. Therefore, they end up making losses when their predictions fail to realize.

In some cases, a casual relationship between two events may exist. However, when individuals overanalyze this relationship, they establish a stronger dependence than what is actual. Based on this, they make decisions related to the future that involves using selective historical information. Usually, this decision follows substantial losses for individuals based on the extent of their decision.

The gambler’s fallacy can also affect investors and financial decisions. For investors, having accurate and complete information about their investments is crucial. When they fail to realize that two investments are statistically independent, they will make wrong decisions. In that case, they end up suffering losses.

How to avoid the Gambler’s Fallacy?

The primary response to the gambler's fallacy is considering more information. One of the reasons this bias may exist is the lack of information that individuals let influence their decisions. By analyzing all available information, individuals can determine the relationships that exist between historical and future events.

Similarly, establishing independence between two events also helps avoid the gambler's fallacy. It is possible to do this by either considering all information or using the critical thinking process. Individuals must also establish the reason why they believe a past event may bear a relationship with a future one. By doing so, they can recognize if they are allowing the gambler's fallacy to impact their decisions.

Conclusion

In most decision-making processes, establishing a relationship between two events is crucial. However, some people may put an overreliance on a relationship when none exists. It primarily stems from the gambler's fallacy, which occurs when individuals believe the probability of a random event in the future depends on another historical event.

Originally Published Here: What Is the Gamblers’ Fallacy?

Disposition Effect in Behavioral Finance

What is Behavioral Finance?

Behavioural finance is a field in behavioural economics that deals with psychological influences and biases. It also examines how these biases impact financial behaviours that investors demonstrate. Furthermore, it studies how these behaviours affect the financial markets, such as the stock market. Behavioural finance also explains how influences and biases cause market anomalies in financial markets.

Behavioural finance also comes in the form of a theory that infers investors are "normal". By stating that, it goes against some other traditional economic theories. Among other things, it also suggests that investors let biases affect their decisions. These biases may come from various sources. One of these includes the disposition effect.

What is the Disposition Effect in Behavioral Finance?

The term disposition effect refers to the bias that individuals have in prematurely selling assets that have made financial gains. This effect relates to flawed decisions made by individuals. The disposition effect primarily affects financial markets and investors. In that regard, it describes how investors sell securities as soon as the price rises. By doing so, they sacrifice any potential gains that they would get from holding it longer.

The disposition effect seeks to explain the rationale behind how investors tend to treat unrealized gains and losses on their assets. More specifically, it suggests that investors are likely to realize gains quickly to lock them. However, they tend to hold to loss-making assets longer, hoping it would turn profitable in the future. In both these cases, the disposition effect causes losses for investors.

How does the Disposition Effect work?

How the disposition effect works can best be explained and understood through an example. An investor wants to free up some funds to invest in a potential high-return asset. However, they don't have the funds necessary to do so. Instead, they must sell one of the two stocks that they are holding. Both of these stocks are of the same value but have different features.

When the investor bought the first stock, they expected to make significant returns in a few years. Until now, the investment has only increased slightly in value. On the other hand, the investor also bought the second stock with a similar view. However, it has suffered considerable losses since the investor acquired it.

Logically, the investor would want to get rid of the loss-making stock because it can potentially amplify the losses. Similarly, they would prefer to hold onto the first stock as it can potentially increase returns. Due to the disposition effect, however, the investor chooses to dispose of the first stock. The first reason why they do so is to lock the current returns from it. On top of that, they hold onto the second stock, hoping it would turn around and be profitable.

How to avoid the Disposition Effect?

The best way to avoid the disposition effect is to dispose of loss-making investments in the long run. It does not suggest that investors should dispose of their assets as soon as they make a loss. However, they shouldn't stick to any investments that have suffered from losses continuously. On top of that, investors must not get rid of assets as soon as they become profitable. By doing so, they can avoid any losses and increase their gains.

Conclusion

The disposition effect in behavioural finance refers to the bias that individuals demonstrate when making decisions. This effect is relevant to the financial market and investors. When investors sell profit-making assets prematurely or hold onto loss-making assets, the disposition effect comes into play. Investors can easily avoid the disposition effect by avoiding both of these circumstances.

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What is Recency Bias?

What is Recency Bias?

Recency bias represents the tendency of individuals to remember information that they have analyzed. While considering recent information can provide short-term results, it may neglect long-term effects. However, some people tend to allocate too much reliance to recent occurrences, which may cost them in the long run. Therefore, individuals' tendency to place an overemphasis on new information incorporates recency bias.

Another name for the recency bias is the recency effect. This bias is prevalent in almost any field that involves making decisions based on historical information. It is human nature to remember and recall the most recent information and forget historical info. Hence, recency bias is an influence that occurs naturally. Recency bias also explains how individuals' memory works.

How does Recency Bias work?

As mentioned, recency bias depends on human memory, specifically short-term memory. This memory represents an individual's ability to hold a small amount of information for a brief period of time. However, there is a limited amount of data that it can store and a limited period for which it can do so. The more individuals delay recalling the information, the more of it they will forget.

Recency bias works on the same principle. Individuals tend to remember information that they have most recently experienced. It is because that information has stayed in their memory due to being fresh. Any other relevant information may have already left their memory due to its limited capacity. On top of that, any information they have come across several times will stay in their memory for longer.

In short, recency bias occurs when individuals remember any information that they have recently encountered. The more they stay away from it, the more they will tend to forget. In that case, they will be more prone to recency bias than before. However, the recency bias for each individual may differ due to their memory capacity.

What are the factors that influence Recency Bias?

There are several factors that contribute to how much recency bias individuals face. Usually, the type of information and its purpose play a critical role in recency bias. The more complicated the process is, the more likely it is for individuals to forget information. Therefore, the more risk of coming under the influence of recency bias is.

The method the individuals use to process information also plays a role in determining the recency bias involved. Another factor that plays a significant role in recency bias is the time period for which individuals retain information. The longer this time period is, the higher their tendency is to forget information and higher recency bias.

Lastly, for multitasking individuals, the recency bias may be at its highest. It is usually because these people tend to process information from multiple sources at once. Due to this, they are likely to retain a small amount of information from each source. Therefore, the recency bias for them will be higher. All of these factors depend on each individual and how their memory works.

Conclusion

Recency bias is when individuals tend to prefer recent information and disregard long-term info. In most cases, recency bias occurs due to individuals' capacity to hold information. The longer the period for which individuals must retain information is, the higher the chances of recency bias transpiring are. On top of that, several other factors may contribute to the level of recency bias.

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What is Status-Quo Bias?

Many individuals dread change. Therefore, they will go through any process to prevent change from occurring. The reason why an individual may do so depends on several factors. Instead of welcoming change, these people will try to ensure things remain the same way. When these changes do occur, they view it as a loss or evil. This preference of things staying the same can lead to a bias in decision-making.

What is Status-Quo Bias?

Status-quo bias is a type of influence that individuals get from preferring stability over change. As mentioned, when choosing between change and conservation, they will always opt for the former. For most individuals, change comes with uncertainty, which they may not like. Instead, they strive to ensure things remain the same for the longest possible time.

Status-quo bias comes as a direct result of being resistant to change. In any decision-making process, change is inevitable. However, individuals will always prefer a course that ensures there is no or minimum change involved. This way, they may end up making decisions that can lead to losses in the future. Overall, status-quo bias can lead to irrational decision-making.

How does Status-Quo Bias work?

Status-quo bias comes as a direct result of the change. Therefore, any process that involves change also includes the status-quo bias. The term status-quo bias comes from the work of William Samuelson and Richard Zeckhauser. They conducted various experiments which suggest that people show a substantial preference for choices that maintain the status quo.

The research also suggests that when faced with critical decisions, people will prefer certainty. Therefore, they will always choose a decision that does not involve any change. While doing so can make the process shorter and more manageable for individuals, it also implies they may compromise their decision-making.

Status-quo bias can entice individuals to make decisions that aren't in their best interest. By sticking to stability, individuals also sacrifice the opportunities that present themselves through change. Usually, individuals establish a default state, which they consider stable. If any decision alters this state, they will likely oppose it despite it being more reasonable.

How to avoid Status-Quo Bias?

Like any other type of bias recognized by behavioural finance, individuals can avoid the status-quo bias by understanding how it works. Similarly, by weighing all available options against each other and considering them objectively, individuals can reach better decisions. This way, they can avoid sticking to a default state and welcome change that accompanies opportunities.

Similarly, having a plan or course of action can help in avoiding status-quo bias. This way, individuals can see the overall picture of how decisions will impact them. Furthermore, if individuals consider the losses associated with stability, they can make more informed decisions. Overall, status-quo bias is avoidable and can result in higher returns.

Why is Status-Quo Bias important?

Status-quo bias is important because it prevents individuals from welcoming change. Therefore, it can lead to missing out on new opportunities and sticking to a default state. Understanding this bias can help individuals prevent it from occurring. This way, they don't neglect any potential returns that they can get from otherwise embracing change.

Conclusion

Status-quo bias is the type of bias that tempts individuals to prevent change from occurring. Instead, it forces them to stick to a default state. Status-quo bias can impact individuals’ decisions and lead to missed opportunities. Understanding this bias and how it works is crucial for individuals as it can help them avoid it.

Originally Published Here: What is Status-Quo Bias?

What is Overconfidence Bias?

What is Overconfidence Bias?

Overconfidence bias occurs when individuals are overconfident of their talent, skills, or abilities. Usually, these individuals overestimate their beliefs and judgments than what is objectively reasonable. Their perception comes from a subjective viewpoint. The problem occurs when they let this confidence influence their decisions and other transactions.

Overconfidence bias comes from a self-interest perspective. People tend to believe they are better than they actually are. Hence, they perceive their knowledge and skills as superior. Once individuals let this bias affect their decisions, they can make wrong decisions. As a result, they increase the chances of loss that they will make based on those decisions.

How does Overconfidence Bias work?

Overconfidence bias is relevant in various fields of life. When individuals come across decisions or events, they need their skills, abilities, experience, etc., to make the right decisions. Ideally, people must make decisions based on evaluating all of these factors properly. In some cases, however, they may end up overestimating them and lead to wrong decisions.

Overconfidence bias drives people towards ignoring significant indicators that would affect their decisions. Instead, they end up relying on their skills or abilities to do so. However, they put overreliance on it and usually overvalue them. Overconfidence bias also depends on people's moral character. It tempts people to act in a way that does not consider all relevant factors.

How does Overconfidence Bias impact investors?

Overconfidence bias also applies to finance and investing. For investors, making the right decisions based on relevant information is crucial. Usually, they consider several factors using analytical tools and use them to form an opinion. However, some investors may underestimate the impact of such analysis and ignore them. Instead, they may put an overreliance on their skills in analyzing stocks.

By neglecting all the crucial factors, investors can make wrong decisions. Similarly, they may end up misestimating their risks and returns from investments. There are several types of overconfidence that investors may illustrate. Based on the type they demonstrate, they will experience varying results. Overall, overconfidence bias can also be influential for investors.

How to avoid Overconfidence Bias?

The simplest solution to overcome overconfidence bias is for individuals to evaluate their skills and abilities. This way, they can form an opinion on how much they can rely on these skills or abilities in decision-making. However, doing so may not be possible practically. Nevertheless, investors can still evade the impact of overconfidence bias.

For investors, understanding the risks associated with the investments is also crucial in avoiding this bias. Investors can employ various risk management strategies to evaluate their risks adequately. It is because investors tend to view their investments as being less risky than they actually are. This way, they won't put an overreliance on their skills and abilities.

What are the types of Overconfidence?

Individuals may demonstrate various forms of overconfidence in their decisions. These may include the illusion of control, planning fallacy, or contrary evidence related to overestimation. Some investors may also demonstrate timing optimism, desirability effect, or over-ranking. These factors also contribute to individuals’ overconfidence.

Conclusion

Overconfidence bias is when individuals overestimate their skills, abilities, or talent. Based on this overconfidence, they make decisions that come from a subjective viewpoint. Overconfidence bias can also occur in investing. When investors are overconfident of their skills, they neglect information that can be crucial. Overconfidence bias is avoidable in various ways, as mentioned above.

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What is Loss Aversion Bias?

When investing, each investor will establish a risk appetite that defines the risk they are willing to take. Several factors contribute to how much uncertainty investors will accept. Based on that, some investors will face risks and get rewards for them. For some others, the uncertainty involving losses may be critical. Therefore, they may prioritize the risk of losses rather than their returns.

What is Loss Aversion Bias?

Loss aversion bias is when investors tend to prioritize avoiding losses in their transactions. During this, they try to avoid losses rather than focusing on making gains. Investors may face such bias due to several reasons. However, one of the primary factors for loss aversion bias is previous experiences. Investors that have suffered due to losses in the past are highly likely to allow loss aversion bias to affect their decisions.

Loss aversion bias describes why individuals amplify the damages they suffer on their investments. Some investors may also perceive losses as twice as impactful as the gains they make. This bias may differ for each individual as it describes an individual's subjective tendency to prefer loss avoidance. When choosing between avoiding losses and making gains, these individuals will prioritize the former.

How does Loss Aversion Bias work?

Loss aversion bias is a part of behavioural finance. When faced with various financial decisions, investors may allow their experience with losses to influence their decisions. Similarly, the risk appetite that individuals have is also likely to affect their decisions. Due to this, they may avoid making decisions that can promise twice the gains over losses.

Loss aversion bias comes from individuals' preferences and risk appetite. Due to this bias, they end up overestimating the impact of losses on their investments. Therefore, they prioritize the losses they can avoid rather than the gains they make. There are several factors that influence this bias in individuals, including their past experiences and the stakes involved.

Some third parties may also manipulate investors to exploit their loss aversion bias. This way, they can force investors to take wrong decisions and make losses. These decisions can come in two forms. One form includes enticing investors to sell securities despite potential gains. Another form comes in tempting investors to hold onto their investments despite making huge losses.

How can investors avoid Loss Aversion Bias?

There are several methods that investors can use to avoid loss aversion bias. Firstly, investors can evade the influence of loss aversion by understanding how it works. Similarly, planning and critical thinking can also reduce the impact of loss aversion bias. In some circumstances, investors can also employ risk management strategies to mitigate their risks.

These strategies may include the use of hedge instruments to offset their losses. Similarly, investors can reduce their risks by identifying relatively low-risk investments and making them a part of their portfolios. For some investors, obtaining certainty that comes with low-risk investments can also mitigate the effects of loss aversion bias. Overall, loss aversion bias is manageable through using a combination of risk management strategies.

Conclusion

Loss aversion is a term used to represent when investors overestimate the potential losses related to their decisions. In some circumstances, they may put double emphasis on their losses relative to the potential earnings they make. Therefore, they may avoid making rational decisions that can result in significant gains. Loss aversion, like any other type of bias, is avoidable in various ways.

Originally Published Here: What is Loss Aversion Bias?

How Retail Trading Affects Stock Market Volatility and Trading Strategies

Retail traders are often called noise, or uninformed traders as they contribute in large part to the noise in the stock market. The pandemic just accentuates this effect. A recent paper [1] examined the impact of retail trading on the market during the pandemic. It uses Robinhood traders as the proxy for retail investors and studies the impact of their activities on the stock market. The article concluded that retail traders increased the stock market volatility and reduced its efficiency.

In this study, we examine the relation between Robinhood usership and stock market volatility. We assume that the average trader on Robinhood is at an informational disadvantage relative to other professional traders. The noise trading theory suggests that uninformed investors may create systematic risk by coincidentally responding to the same noisy signal (see e.g., DeLong, Shleifer, Summers, and Waldmann, 1987; Kelly, 1997). In agreement with this assertion, we find that Robinhood usership has a negative impact on stock volatility both in levels and percent change tests. On high Robinhood user trade activity days, volatility increases substantially.

The article also predicted that the number of retail traders will increase, leading to a higher stock market volatility,

The number of retail traders, or even Robinhood traders is only forecasted to increase. If our findings continue in relevancy, the degree of which we observe volatility will only increase, and this is without considering further extreme examples like GameStop or other unpredictable macroeconomic factors, taxing policy, scandals, etc.

The increased market volatility caused by retail traders can result in difficulties for professional traders and investors, for example,

• Designing and predicting the market will become more difficult, especially when we use trading strategies that rely heavily on historical backtests,
• Shorting volatility will likely suffer bigger losses.

As for point #1, we can mitigate the impact of unpredictable price and volatility movements by developing strategies that rely less on historical data and more on market principles.

Regarding point #2, one might think that going long volatility can lead to big gains, but this might not be the case. Designing a profitable long-volatility trade is not trivial.

References

[1] Jones, Cooper, Retail Trading and Stock Volatility: The Case of Robinhood (2021). All Graduate Plan B and other Reports. 1534.

Originally Published Here: How Retail Trading Affects Stock Market Volatility and Trading Strategies

What is Mental Accounting Bias?

What is Mental Accounting Bias?

Mental accounting is a process that individuals use to assign subjective values to their money. However, these values go against the accepted economic principles. Mental accounting is a concept that comes from behavioural economics. Often, people tend to place varying values on the same amount of money based on their preferences. However, this process can lead to losses.

Mental accounting leads to a behavioural bias that can influence the decisions that individuals make. It is also known as the "two-pocket theory". In mental accounting, people tend to classify their money and put them in mental accounts. The classification may occur on a different basis, such as where the money comes from or how they intend to use it.

How does Mental Accounting Bias work?

The concept of mental accounting bias comes from the work of an economics professor, Richard Thaler. He defined mental accounting as "the set of cognitive operations used by individuals and households to organize, evaluate and keep track of financial activities". Therefore, mental accounting works through the classification or categorization of money that individuals make based on their beliefs.

Mental accounting bias influences individuals to make irrational decisions when it comes to their finances. Once they set aside money for a specific purpose, they fail to consider any drawbacks of using it. Similarly, they fail to consider their decisions as a part of an overall goal. Furthermore, individuals perceive losses and gains in different terms. All of these perceptions depend on the situation in which they are.

Mental accounting is also closely related to the concept of the fungibility of money. This concept suggests that money is the same regardless of where it comes from or how individuals intend to use it. However, mental accounting goes against it because individuals differentiate between money based on these factors. By understanding this, individuals can also avoid the influence that mental accounting has on their decisions.

How does Mental Accounting apply to investors?

Mental accounting bias is prevalent for investors. Some investors like to classify their portfolios into two categories. One includes safe stocks that they can use as a substitute for any losses from the other category. The other classification is speculative portfolios that investors use to make high-risk investments.

Mental accounting can lead to investors taking irrational decisions. When investors compare losses and profits, they end up perceiving both differently. Although both may have the same monetary values, investors tend to make categories for each based on a mental account. Therefore, it leads investors to think in relative terms rather than viewing decisions in absolute terms.

How to avoid Mental Accounting Bias?

Mental accounting can occur for all individuals. However, it is still avoidable. Individuals that demonstrate mental accounting can use deliberate planning to break bad financial habits. These may include creating budgets or making plans for any unexpected income or gains they receive.

Furthermore, individuals that suffer from mental accounting can also look at the whole picture. This way, they can perceive decisions in absolute terms rather than relative terms. Using all of these techniques, individuals can avoid the adverse effects of mental accounting bias.

Conclusion

Mental accounting bias is when individuals assign subjective values to their money. These values may come from how they perceive their money. Mental accounting bias can cause several problems and result in losses for individuals. It is also common for investors to illustrate such behaviour. However, mental accounting bias is avoidable through proper planning and deliberate decision-making.

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What is Hindsight Bias?

In finance and investing, making accurate predictions based on certain factors is critical. If an investor makes the correct prediction, they can expect high returns. However, wrong forecasts can also be vital, as they can cause losses. There are several factors that may influence whether someone makes the correct predictions. However, there are some influences that may cause adverse effects. One of these includes the hindsight bias.

What is Hindsight Bias?

The term hindsight bias refers to the tendency of people to think of unpredictable events as predictable after they have occurred. In this bias, individuals pretend they knew the outcome after its occurrence. It also happens when individuals assume they have exclusive insight in predicting an outcome. Based on this bias, they make wrong predictions in the future and end up suffering losses.

Hindsight bias is crucial in various aspects of life. However, it can be more influential for investors that decide on their portfolio. Once an event occurs, investors may assume they knew the outcome from before. Based on this confidence, they make decisions about the future. These decisions don't turn out to be accurate, which leads to losses.

How does the Hindsight Bias work?

Hindsight bias starts with an event with an unpredictable outcome. Once the event occurs, individuals may assume they predicted the result. This outcome may or may not be in line with their original prediction. However, since they know the final result after the event, they assume that the consequence was predictable. The bias occurs when these individuals allow this confidence to influence future decisions.

In finance, investors face hindsight bias after they see their predictions realize for stocks. However, due to the high frequency of transactions, the right forecasts make a small proportion of the total predictions. Nonetheless, investors use it to base further decisions. They believe their predictions will come true because of that one-off event.

There are several factors that contribute to the existence of hindsight bias. These may come from cognitive, metacognitive, or motivational variables. When these factors occur at the same time, the chances of hindsight bias are the highest. Investors that allow these variables to influence their decisions can end up making the wrong investments. Through that, they can suffer considerable losses.

How to avoid Hindsight Bias?

There are several ways in which individuals can avoid hindsight bias. Like other biases, understanding how this bias affects the decision-making process is crucial. People who are aware of this bias are also likely to prevent it from influence their decisions. Similarly, having and considering all the available information can be critical in avoiding any bias, including hindsight.

Hindsight bias occurs after an event has occurred, and individuals perceive the outcome as predictable. However, instances, where people get the correct prediction, are significantly lower than when they get it right. Therefore, considering both of these instances can be critical for individuals. Once individuals learn from their wrong predictions, they can avoid hindsight bias from affecting their decisions.

Conclusion

Hindsight bias is when individuals consider an event as predictable after its occurrence. Based on this assumption, they transfer the confidence to other transactions or events. It then leads to them making wrong decisions and bearing losses. Hindsight bias is avoidable like other biases. Individuals have to be aware of its existence and how it affects decision-making.

Article Source Here: What is Hindsight Bias?

The Illusion of Control Bias

What is the Illusion of Control Bias?

The illusion of control bias is a type of bias in behavioural finance that gives people the illusion of control. People that inherit this behaviour tend to overestimate the power or control they have over a circumstance. Therefore, they tend to think they have more control over events than they actually do. Even in situations where randomness or uncertainty plays a significant role, people tend to overestimate their control level.

In behavioural finance, the illusion of control bias is one of the positive illusions. These are illusions in which people have favourable attitudes towards themselves even when they are unrealistic. The illusion of control bias affects people of all fields. In finance and investing, where uncertainty is prevalent, this bias can be critical.

The term “illusion of control” comes from the work of Ellen Langer, a Harvard psychologist. In this work, the author conducted various experiments to check how this bias occurs. She found that there are several factors that play a role in people’s confidence. However, none of these included the end result. Instead, they focused on things that happened during the process.

How does the Illusion of Control Bias work?

The illusion of control occurs when people overload themselves with a substantial amount of information. When this happens, their brains start taking shortcuts that help them in the decision-making process. Taking shortcuts helps reduce the time and stress for these people. However, it also exposes them to the impression that they have control over the process.

In most circumstances, when people believe they have more control over something than they really do, they take shortcuts. They do so instead of taking their time to find logical solutions or contemplating alternative outcomes. For people, it also leads to believing in superstitions or paranormal events. Therefore, the illusion of control can give them a fake sense of control in circumstances where they don’t have any.

Why is the Illusion of Control Bias important?

The illusion of control bias shows how people make irrational decisions that lead to adverse outcomes. Most of these decisions come from overconfidence in the process that people put into it. Most people tend to ignore factors that would suggest not taking the decision. Instead, they end up placing confidence in decisions where chance or possibility plays a substantial role.

The illusion of control bias is also critical as third parties can use it to influence people's decisions. Factors such as competition, choice, stimulus, or response familiarity affect how much confidence people put in their decisions. When people don't understand the implications of their confidence, they end up making the wrong decisions.

How can individuals avoid the Illusion of Control bias?

Like most other biases, people can avoid the illusion of control bias by understanding how it works. They can also consider external sources of information rather than relying on their instincts. Similarly, through critical thinking during these decisions, people can avoid the illusion of control bias. It includes not establishing casual links between choices and outcomes when none exist.

Conclusion

The illusion of control bias refers to when people overestimate the control they have over an event. In that case, they end up taking the wrong actions, which leads to losses. There are several factors that may cause this type of bias to occur. Therefore, it is crucial for decision-makers to understand these and not allow them to influence their decisions.

Originally Published Here: The Illusion of Control Bias

What is Representativeness Heuristic Bias?

Behavioural finance refers to the study of psychological influences and biases that affect the behaviour or decisions of investors. It also studies how these influences affect the market. One of the primary areas in behavioural finance is the study of biases. A behavioural bias is an irrational belief that can affect an investor's decision making, mostly unconsciously. One such bias comes from the representativeness heuristic.

What is Representativeness Heuristic?

Representativeness heuristic refers to the process individuals go through when estimating the likelihood of an event. When faced with such decisions, individuals usually use mental shortcuts or preexisting beliefs to make comparisons. This preexisting belief or mental shortcut is known as representativeness heuristic. This process consists of making decisions based on how they compare to beliefs that individuals have in their minds.

Representativeness heuristic can be significantly helpful in any decision-making process. It is because it helps make the process faster and, thus, shorter. However, it can also lead to individuals making wrong decisions. Heuristics, or these mental shortcuts, can help in many fields. However, in deliberate decision-making, they can cause biased decisions.

How does Representativeness Heuristic cause bias?

Representativeness heuristic involves finding similarities between decisions and acting accordingly. However, it can cause biased opinions, especially when individuals establish resemblance even where none exists. There are many reasons why individuals may make such comparisons. However, the primary cause is having preexisting beliefs or opinions about a specific object or event.

Another reason why representativeness heuristic bias may occur is due to time or resource constraints. When individuals don't make decisions by comparing all the available information, it causes them to establish biased opinions. These opinions usually come from existing beliefs or mental shortcuts. Although there may be no or minor similarities between their existing beliefs and the specific decision, they still make comparisons.

How can individuals avoid Representativeness Heuristic Bias?

Like most other biases, one of the most efficient protection against representativeness heuristics is knowing it exists. When individuals are aware of the tendency to make comparisons even when none exist, they will make better decisions. Similarly, reflecting on their judgments for every decision can be helpful in establishing whether they have a biased opinion.

Another counteraction against this bias is being open to information. Often, individuals ignore information that can change their decisions. The primary cause of this comes from their tendency to compare new events with preexisting beliefs. Once they consider new opinions or feedback, they can make better decisions. Lastly, through critical thinking and applying logic to decisions, individuals can avoid representativeness heuristic bias.

Why is Representativeness Heuristic Bias crucial?

The representativeness heuristic is crucial for individuals from all fields for several reasons. One of the primary reasons is that it gives rise to various other biases. For example, the representativeness heuristic can lead to conjunction fallacy or gambler's fallacy. Furthermore, it can also be used to influence or manipulate individual’s opinions.

For investors, the representativeness heuristic can also be crucial. During the investing process, investors come across various options that may have some resemblances. Usually, however, these similarities are not prominent. Despite that, investors end up making decisions based on them. Consequently, it leads to substantial losses for them.

Conclusion

The representativeness heuristic is a process in which individuals use mental shortcuts when estimating the possibilities of events. It can cause them to make comparisons and seek out similarities where there are none. The representativeness heuristic is avoidable in several ways. Despite that, individuals let it influence their decisions.

Originally Published Here: What is Representativeness Heuristic Bias?

What is an Example of Confirmation Bias

Behavioural finance is a field of behavioural economics that deals with the psychological influences and biases that affect investors. These biases exist in the decisions that investors make and can cause them to make the wrong choices. There are several types of biases that investors may face during their investing process. One of these includes confirmation bias.

What is Confirmation Bias?

Confirmation bias is a type of bias that occurs when investors prefer information that confirms a belief they have had previously. Their belief may not be correct. However, since they come across information that supports it, investors tend to favour that information. Some investors may also actively seek out information that confirms their beliefs.

Confirmation bias also represents the bias that investors may have against any information that does not support their beliefs. Usually, investors tend to put more focus on any information in support of their views. When they come across information that contradicts their belief, they ignore it. Confirmation bias can, therefore, lead to irrational investment decisions by ignoring crucial information.

How does Confirmation Bias work?

Confirmation bias starts from a preexisting belief that individuals have. These beliefs may or may not be true. When looking for investments, these individuals will look for information that supports their views about the decision. During the process, if they come across any information that disregards their beliefs, they tend to ignore it. Usually, they don't do so deliberately. Instead, this process occurs unintentionally, and they don't have any control over it.

When investors come across any information that confirms their beliefs, they gain confidence in it. They may look for further evidence to support their preexisting views. The more information they come across in support of it, the more reliance they develop over it. As a result, they will end up making the wrong decisions. These decisions can relate to buying or selling stocks or the type of investment they make.

Confirmation bias doesn't only apply to finance or investing. It is prevalent in all fields of life. One of the simplest ways of protecting against confirmation bias is to realize it exists. However, that may not be possible since it is a subconscious process. Individuals can also reduce confirmation bias in their dealings by obtaining a second opinion or studying contrasting beliefs.

What is an example of Confirmation Bias?

An investor comes across an opinion that suggests that the real estate market will boom in the future. The investor believes the opinion is accurate. Based on that opinion, the investor seeks investments in the real estate market. During the analysis phase, they come across various publications that suggest the real estate market will go down.

Although the information contradicts the original opinion formed by the investor, the investor does not consider it. Instead, the investor actively searches for publications that confirm their view. Eventually, the investor comes across an unverified article that presents the same opinion as to their belief. Based on that information, the investor invests in the market.

However, the real estate market goes down, and all real estate value falls. The investor makes a significant loss for investing in the market. In this scenario, the investor developed confirmation bias when they chose to believe an unverified article instead of several publications.

Conclusion

Confirmation bias is a type of bias that occurs when investors favour information that confirms their preexisting views. They actively disregard any information that contradicts their opinion and instead seek out information that validates it. Based on this, they make decisions, which leads to losses in the future.

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Adaptive Markets Hypothesis

What is Adaptive Market Hypothesis?

The adaptive market hypothesis (AMH) comes from the works of Andrew Lo from 2004. This hypothesis brings together the principles of the efficient market hypothesis (EMH) and behavioural finance. It does so by applying the principles of evolution to financial interactions. These principles include adaptation, competition, and natural selection.

In economics, most traditional financial economics theories contradict the principles set forth by behavioural models. However, with the help of the adaptive market hypothesis, these models can coexist. This theory applies the evolutionary biology framework to specific financial concepts.  Similarly, AMH has five principles, which it suggests are crucial for adaptive markets.

What are the two parts of the Adaptive Market Hypothesis?

As mentioned, the adaptive market hypothesis brings the efficient market theory and behavioural finance. Although these have contradicting views, AMH seeks to explain how these apply to practical situations. A brief description of each of these is as below.

Efficient Market Hypothesis

One of the core parts of the adaptive market hypothesis is the efficient market hypothesis, or simply, the efficient market theory. This theory suggests that all stock prices reflect all information. Therefore, stocks will always trade at their fair value on stock exchanges. It further implies that investors can’t benefit from overvalued or undervalued stocks.

Behavioural Finance

Behavioural finance is a field of behavioural economics, which suggests that psychological influences and biases can affect investor's financial behaviours. Furthermore, it seeks to explain how these influences and biases cause market anomalies. Behavioural finance also proposes that investors are irrational in their decision-making.

What are the principles of the Adaptive Market Hypothesis?

The adaptive market hypothesis has five basic principles. These include the following.

• Individuals act in their own self-interest.
• Individuals make mistakes.
• Individuals learn from the mistakes they make; they adapt, and then they innovate.
• During these innovations or experimentations, the process of natural selection will apply to individuals, institutions, or markets.
• This evolutionary process determines financial market dynamics.

How does the Adaptive Market Hypothesis work?

The adaptive market hypothesis seeks to consolidate the principles of the efficient market theory with behavioural finance. EMH implies that investors are rational and efficient. However, this view comes as a contradiction to the principles of behavioural economics, which suggest they are irrational and inefficient. Both of these beliefs are the complete opposite of each other.

The adaptive market hypothesis further suggests that people are primarily rational. However, they may act or become irrational in some cases. These cases usually involve a reaction to new opportunities opening up or fluctuations in market volatility. The theory also implies that people always act in their self-interest, which is one of its principles.

During their activities, people may commit several mistakes. However, AMH suggests that people learn from those mistakes. Then, they adapt accordingly. They make best guesses based on trial and error. Every time their tactic fails, they will take a different approach next time. If it succeeds, they will use a similar strategy again. This way, the process of natural selection occurs.

Conclusion

The adaptive market hypothesis combines the principles presented by the efficient market hypothesis and behavioural finance. While the efficient market hypothesis states that investors are rational, behavioural finance suggests otherwise. AMH proposes five principles and seeks to apply the evolutionary biology framework to specific finance concepts.

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Valuation of European Options with Stochastic Interest Rates and Transaction Costs

Black-Scholes-Merton (BSM) is a celebrated option pricing model that is used frequently in the financial industry [1]. The model was developed initially for equity options but then has been extended to many asset classes. It is so frequently used, to the point that the practitioners almost do not pay attention to the underlying assumptions of the model. But once in a while, a situation will arise that would bring the assumptions back to the attention. A recent example was last year when the price of the front-month oil futures went negative, and the pricing models were not capable of handling the negative price.

Recall that the underlying assumptions of the BSM model are as follow,

1. The rate of return on the riskless asset is constant,
2. The instantaneous log return of stock price is an infinitesimal random walk with drift; more precisely, the stock price follows a geometric Brownian motion, and its drift and volatility are assumed constant,
3. Arbitrage is not possible (i.e., there is no way to make a riskless profit),
4. Market participants are able to borrow and lend any amount, even fractional, of cash at the riskless rate,
5. Market participants are able to buy and sell any amount, even fractional, of the stock, including short selling,
6. The above transactions do not incur any fees or costs (i.e., frictionless market).

The above assumptions are not always realistic; they are sometimes violated.

Over the years, a large body of academic research has dealt with relaxing these assumptions and extending the BSM model. A recent paper [2] proposed a model that incorporates stochastic volatility, stochastic interest rate, and transaction costs into the BSM model. The authors derived a non-linear partial differential equation and then used a finite-difference scheme, implemented in Matlab, to solve it.

This paper considers the problem of pricing options with transaction costs under the Heston-CIR model, which is a combination of the stochastic volatility model introduced by Heston and the stochastic interest rate model driven by the Cox-Ingersoll-Ross (CIR) process. We obtain the numerical solution to the PDE of the Heston-CIR model with transaction costs by implementing the finite difference scheme in MATLAB. More precisely, we consider the Heston-CIR model with a partial correlation, derive a pricing formula for zero-coupon bonds and analyze the delta hedging portfolio of the Heston-CIR model with transaction costs. We use the replicating technique and substitute the solution of zero-coupon bonds to derive the PDE for European call options. We then obtain numerical solutions to the PDE of the Heston-CIR model with transaction costs by implementing the finite difference scheme in MATLAB.

References

[1] F. Black, and M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy 81, 639–654, 1973

[2] J. Cao, B. Wang, W. Zhang, Valuation of European Options with Stochastic Interest Rates and Transaction Costs, International Journal of Computer Mathematics, 2021

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Utility Theory in Economics

What is the Utility Theory in Economics?

Utility theory is a theory in economics that emphasizes individuals' choices. This theory explains the behaviour of individuals based on the idea that people make choices based on preferences. Each individual has a different preference. Thus, everyone will make personalized decisions. These preferences are inherent to each individual and not changeable.

Utility theory seeks to explain how individuals' decisions and behaviours can change based on their preferences. The primary focus of the utility theory is that individuals will prefer decisions that provide the most utility for their given preferences. In some cases, measuring utility from a given decision may not be possible. However, this theory assumes that individuals behave as if they make a decision by assigning an imaginary number to it.

According to the utility theory, individuals translate different levels of monetary values into "utils". Util is a hypothetical unit that measures utility. Then the individual processes the decision in utility terms. These terms may often contradict the generation of wealth as some individuals have different goals. Based on that utility approach, investors make a decision.

What is Utility?

Utility in economics represents the total satisfaction that consumers get from the use of goods or services. It measures the benefit that consumers can extract from specific goods or services. However, quantifying the utility that consumers get from goods or services is challenging. Despite that, there are several economic models that attempt to measure it.

Most of these models that believe in rational choice assume that investors will always extract the maximum utility from goods or services. However, utility isn’t a concept that only applies to economics. It also has its application in finance and investing. In finance, utility refers to how much investors can benefit from their portfolio’s performance.

How does utility apply to finance?

As with utility in economics, utility in finance also assumes that investors will seek to maximize their utility. In this case, the utility comes in the form of returns from the portfolio. Therefore, utility in finance relates to the high returns that investors expect from their investments. However, these high returns also come with risks. Usually, there is a direct relationship between both of these concepts.

Due to the high risk on high return investments, investors also face the possibility of making substantial losses. Therefore, investors need to understand the utility they can get from various investments. This way, they can make better decisions and choose stocks that meet their requirements. The utility theory in finance assumes that, given a choice, investors will select the highest return investment option. However, they will also factor in their risk attitude.

What are the assumptions made by the Utility Theory?

The utility theory assumes that individuals can rank all decisions in some order based on their preferences. Similarly, this theory also presumes that individuals will also prefer more utility to less utility. The utility theory also makes the convexity assumption on preferences, suggesting that preferences are convex. Lastly, this theory assumes that all individuals base their decisions on rationality.

Conclusion

The utility theory in economics seeks to explain individuals' behaviours based on preference. This theory suggests that individuals seek value in everything and use that to make decisions. The utility theory in economics also applies to finance. In that case, it looks at how individuals base their decisions based on risks and returns.

Originally Published Here: Utility Theory in Economics

What Is Algorithmic Trading

The use of computers in the field of finance and investing has become more prevalent. Like many other fields, the concept of artificial intelligence has also had its applications in these fields. One such way that computers have changed the investing world is the introduction of algorithmic trading. This process has made the process of investing more automated.

What is Algorithmic Trading?

Algorithmic trading is a type of trading process used by investors to conduct investment transactions through computers. In this process, investors use software to analyze stocks and make investing decisions. Before that, investors provide instructions to the software, which it follows during the investing process. The software considers various factors and can allow for dynamic decisions at a quick rate.

Algorithmic trading uses algorithms to analyze investments and identify ones that meet a set of specific conditions. Through the use of algorithms and human instructions, algorithmic trading allows for quicker and better investing decisions. Algorithmic trading is prevalent in various areas, such as high-frequency trading.

How does Algorithmic Trading work?

Algorithmic trading works through software that takes a set of instructions from investors. With those instructions, algorithmic trading processes a large amount of information. Based on that process, it trades at high speeds to allow the investor to profit. Algorithmic trading helps investors in making both buying and selling decisions in the market.

Algorithmic trading can accept specific instructions about price, quantity, risk, timing, type, etc., from investors. On top of that, it can also provide investors with the option of purchasing liquid or profitable investments. Algorithmic trading is available to all levels of investors, including individual and institutional investors.

What are the advantages of Algorithmic Trading?

The biggest advantage of algorithmic trading is the speed and efficiency with which it makes investing decisions. It can process a substantial amount of information in a relatively short amount of time, which is not possible in manual investing. On top of that, algorithmic trading helps in the automation of the investing process, allowing investors to take a passive approach to investing.

Mostly, algorithmic trading is prevalent among institutional investors. They can use it to make large investments while minimizing the market impact. This way, it provides a level ground for everyone to make investments. It also allows investors to specify the rules for the trades they want to make. Similarly, it can incorporate methods to avoid or limit losses that investors make.

What are the disadvantages of Algorithmic Trading?

Although algorithmic trading automates the investing process, it may not have the same benefits as manual trading. When investors specify the instructions for trading, they limit their investment to those criteria. Consequently, the software misses profitable investments that the investor would otherwise catch.

The use of algorithmic trading also increases the speed with which investments get carried out in the market. This speed can also cause issues in the market. For example, a large number of transactions can cause liquid transactions to become illiquid. Similarly, the speed, paired with incorrect instructions, can cause significant losses for the investor in a short time.

Conclusion

Algorithmic trading is a process that investors use to automate their investing through the use of software. An investor can control the investment by providing instructions to the software. Algorithmic trading allows investors to make investments with better speed and efficiency. However, it may also have some disadvantages, as stated above.

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How High-Frequency Trading Works

What is High-Frequency Trading?

High-Frequency Trading (HFT) refers to a method of trading used by investors. This method involves using computer software to buy or sell large numbers of stocks or securities in a short time. Due to this characteristic, it gets the name high-frequency trading. Investors can use algorithmic programs to analyze various stocks and conclude transactions based on a set of instructions.

High-frequency trading is crucial in markets that have a short timeframe for profits. Therefore, having a quick response for this period is critical for investors as it translates into more profits. Similarly, HFT includes a large number of transactions for that short period of time. Usually, institutional investors use this technique to make large profits.

How does High-Frequency Trading work?

High-frequency trading works through the use of algorithmic trading. This system uses software that can process a large number of transactions in a short time. Usually, this system takes instructions from the user. Based on that, it processes a significant number of investments and places orders accordingly. All this process usually occurs within milliseconds, allowing investors to make profits.

Investors can also modify the software to include global markets and trade on them automatically. Due to the ease of conducting transactions on these markets, high-frequency trading allows investors to profit from various sources. The primary source of profits from this method comes from the speed and size of transactions that the software can make.

What are the advantages of High-Frequency Trading?

High-frequency trading allows investors to generate high profits in a short amount of time. Using powerful software, investors can identify even the smallest price fluctuations and place orders. Similarly, it allows institutional investors to gain significant returns on bid-ask spreads. Using algorithmic trading, investors can identify trends and patterns across various markets.

High-frequency trading also allows investors to exploit arbitrage opportunities across markets using patterns. On top of that, it increases the liquidity of trades in the market. Furthermore, it promotes competition within the market, which can lead to better stability. It, in turn, causes bid-ask spreads to decline, leading to better price efficiency.

Similarly, high-frequency trading also allows investors to limit their losses using several strategies. These may include stop-loss or stop-limit orders. This way, investors can close their position at a specific price to prevent losses.

What are the disadvantages of High-Frequency Trading?

High-frequency trading is a topic of interest for many people. Due to the short-term nature of transactions in this technique, there may be several issues that it can cause. Similarly, it uses trends analysis as a base for making decisions. HFT involves dealing with a large number of investments for a short time. While it can be profitable, it can also cause substantial losses for investors.

High-frequency trading also causes damage to other investors in the market. On top of that, it can increase market volatility or cause market crashes. Many regulations have come forward which try to limit or regulate HFT activity.

Conclusion

High-frequency trading is an algorithmic trading method used by investors. Using this method, investors use software to process a significant amount of investment information. Subsequently, they buy large numbers of investments based on the processed information. HFT attempts to identify investments with short-term price fluctuations and place orders.

Originally Published Here: How High-Frequency Trading Works

Volume-Weighted Average Price Formula

What is the Volume-Weighted Average Price?

The volume-weighted average price (VWAP) represents a stock's average price, weighted based on the total trading volume. Usually, investors use it to determine the average price that the stock has traded on the stock market for a day. The volume-weighted average price considers two factors, the stock's price, and its trade volume.

Using the volume-weighted average price for a stock, investors can determine the mean for the stock's price performance. On top of that, it also provides them with a method to identify trends and predict future performance. Furthermore, it allows investors to determine whether they are receiving the best price for their stocks. The volume-weighted average price is critical in algorithm-based trading programs.

How does the Volume-Weighted Average Price?

The volume-weighted average price measures the weighted average price for a stock adjusted by volume. This way, it allows investors to calculate trends and make a decision accordingly. Based on this information, they can buy or sell stocks without any significant impacts. The VWAP also provides a rule for investors to make decisions.

Usually, investors buy stocks when the price falls below the volume-weighted average price. On the other hand, a stock price above this point will mean investors can profit from selling stocks. Using this approach, investors move the average back towards the volume-weighted average price. The volume-weighted average price is a significantly important tool for all investors, whether individual or institutional.

What is the Volume-Weighted Average Price formula?

The volume-weighted average price formula considers the average price and volume of a specific stock for a single day. It includes all of this information from the time the market opens up to when it closes. The VWAP formula only uses intraday information to calculate the average price. The volume-weighted average price formula is as below.

Volume-Weighted Average Price = ∑ Price x Volume / ∑ Volume

For the above formula, investors need to accumulate the price and volume data of a specific stock. After doing so, they must calculate the product of the price and volume for the data.  Next, they must add all these products to obtain the numerator for the above formula. Similarly, they must sum up all the volume information to get the denominator part.

Investors can also calculate the VWAP continuously throughout the day. For that, they must calculate the average price of the stock traded for the first five-minute period of the day. For that, they must calculate the sum of the high, low, and close price and divide it by three to obtain an average. Next, they must multiply it by the volume for the period. They can use this to calculate the VWAP for that period.

Once calculated, investors can add the product of price and value from each period to the values calculated before. Next, they can calculate the updated volume-weighted average price formula. Throughout the day, investors can keep adding updated figures to the calculation to get the latest price.

Conclusion

The volume-weighted average price is an average price for a stock that investors calculate based on a total trading volume. This price allows investors to determine the mean position for a stock’s price and make investment decisions accordingly. It also helps investors in confirming stock trends and maximizing their returns.

Post Source Here: Volume-Weighted Average Price Formula

What is Value Vs Growth Stock

Investors that are active in the stock market will come across two types of stocks. These include value and growth stocks. Both of these stocks provide varying risks and rewards. On top of that, there are several other differences that set these stocks apart. While there are no specific definitions for what value or growth stocks are, there are some distinguishing characteristics that each of these stocks illustrates.

What is Value Stock?

Value stocks represent any shares traded in the stock market that have a market value lower than their intrinsic value. Intrinsic value represents the present value of all expected cash flows from that stock discounted at an appropriate discount rate. As the market price for these stocks is lower than what they should be worth, value stocks represent undervalued stocks.

Due to the lower prices, value stocks have the potential to realize their intrinsic value. If an investor buys value stocks, they have the opportunity to benefit if the stock’s intrinsic and market values equalize in the future. The primary source of return on value stocks is the income that investors will get from the price differences.

What is Growth Stock?

Growth stocks represent any shares traded in the market that have a significantly higher estimated growth rate. Usually, most stocks in the stock market promise growth. However, growth stocks illustrate a higher growth rate than the market average. It means that these stocks have the potential to outperform the market in the future.

Unlike value stocks, however, growth stocks have similar market and intrinsic values. Therefore, investors do not have the opportunity to benefit from any profits from price differences. Sometimes, growth stocks may also come with a valuation higher than their intrinsic value due to the higher potential growth in the future. The primary source of return for investors with growth stock is the future growth in earnings.

What are the differences between Value and Growth Stock?

The above definitions of value vs growth stocks should allow investors to understand the differences between these stocks. However, there are some other characteristics that distinguish value and growth stocks. One of these differences includes the methods investors use to differentiate between value vs growth stock.

Usually, investors calculate the intrinsic value of several stocks and compare it to their market value. On top of that, they may use ratios such as the price-to-earnings ratio, price-to-equity ratio, current ratio, etc. Based on these, investors can determine if a stock is undervalued. In contrast, investors look at metrics such as earnings per share, earnings before taxes, return on equity, etc., to identify growth stocks.

Value and growth stocks also differ according to the company's size. Usually, value stocks come from large, well-established companies. The difference in their stock valuation may come from temporary factors. On the other hand, growth stocks may consist of companies of all sizes. However, these are not established companies. Instead, these companies have a substantial potential to grow in the future.

Lastly, value and growth stocks are different in the risks they involve. As mentioned, value stocks are from well-established companies. Therefore, the risks involved in value investing are lower. However, these also provide lower returns. On the other hand, growth stocks have higher risks but come with potentially higher returns.

Conclusion

Investors often confuse value and growth stocks. However, both of these are different from each other based on several characteristics. Value stocks represent undervalued investments that will return to their market value in the future. Growth stocks, on the other hand, are stocks that promise higher growth than the market average.

Article Source Here: What is Value Vs Growth Stock

What are Valuation Multiples

What are Valuation Multiples?

Valuation multiples are a group of ratios or multiples used to evaluate a company. For investors looking to compare between various investments, these multiples provide a comparison method. Valuation multiples consist of several tools to evaluate a company using financial metric comparisons. These multiples include assessing a single financial metric as a ratio of another metric for specific purposes.

The aim of using valuation multiples is to make comparisons between several companies or businesses straightforward. These multiples aim to provide a company's financial and operating characteristics using a measurable ratio. However, investors must use metrics that have a logical relationship with a company's value to provide accurate measurements.

What are the types of Valuation Multiples?

There are various types of valuation multiples that provide investors with a tool for evaluation. These multiples come in two categories, including equity multiples and enterprise value multiples. Both of these are relevant tools for investors to use. However, they provide varying results as the former bases calculations on equity while the latter uses enterprise value.

Equity Multiples

Equity multiples are relevant when investors make decisions about investing in stocks. These multiples base calculations on equity and do not consider other factors that enterprise value does. Therefore, it provides investors with a tool to only consider equity investments. These multiples involve calculating the ratios between a company’s performance and its share price.

Some of the well-known and commonly used equity multiples are as below.

Price-to-Earnings Ratio

The price-to-earnings ratio (P/E) considers a company's stock price in relation to its earnings per share for a period. It is one of the most prominent ratios used by investors when evaluating company stocks.

Price-to-Book Ratio

The price-to-book ratio is another commonly used ratio that compares a company’s stock price to its book value per share.

Price-to-Sales Ratio

The price-to-sales ratio considers a company’s stock price in relation to its revenues.

Enterprise Value Multiples

Enterprise value multiples vary from equity multiples. Enterprise value multiples consider a company’s enterprise value rather than its stock price. Through that, it also incorporates a company’s debt. For that reason, it is a better measure of a company's total value.

Some enterprise value multiples include the following.

Enterprise Multiple

The enterprise multiple provides a company's value by dividing its enterprise value by its EBITDA.

Enterprise Value-to-EBIT

The enterprise value-to-EBIT ratio is similar to the enterprise value. However, it includes depreciation and amortization in the earnings when calculating the ratio.

Enterprise Value-to-Sales

The enterprise value-to-sales ratio is another commonly used valuation method. It considers the ratio of a company’s enterprise value to its sales. This multiple is useful for companies that have a negative EBIT or EBITDA.

Why do investors use Valuation Multiples?

There are various reasons why investors may use valuation multiples. These multiples provide valuable insights and information about a company. Valuation multiples also allow investors to quantify a company's performance and valuation. On top of that, these are easy to calculate for any company using financial statements. Consequently, valuation multiples also provide a way for investors to compare various options.

Conclusion

Valuation multiples are a group of financial metrics that allow investors to evaluate a company. There are many valuation multiples that investors can use. These come in two categories, namely equity and enterprise value multiples. Each of these categories has specific uses. Investors use valuation multiples for various reasons, for example, comparisons, evaluations, etc.

Article Source Here: What are Valuation Multiples