Technical Trading Strategies: Profitable or Just Curve-Fitting?

Technical analysis (TA) is a trading approach that evaluates past price movements and volume data to forecast future market trends. It relies on price patterns, indicators, and statistical measures rather than fundamental factors. Investors use tools like moving averages, RSI, and Bollinger Bands to identify trends, support and resistance levels, and potential entry or exit points.

The growing proliferation of academic articles on technical analysis suggests its acceptance and effectiveness. However, some researchers continue to question its validity. Reference [1] revisits the question of whether TA works, testing TA rules on three stocks—AAPL, MSFT, and NVDA—from January 2000 to December 2022. The study evaluates the profitability of various technical trading strategies both in-sample and out-of-sample using methods such as reality checks and stepwise tests.

The authors pointed out,

In this paper, we analyze a comprehensive dataset of AAPL, MSFT, and NVDA stocks from January 2000 to December 2022 to evaluate the profitability of various technical trading strategies both in-sample and out-of-sample, using February 2016 as the primary cutoff and May 2018 as an alternative. We construct strategies based on multiple indicators and timeframes, conducting thorough statistical analyses to ensure robustness against data-snooping bias. Our results consistently demonstrate that apparent profitability often stems from parameter selection rather than true market inefficiencies, supporting the efficient market hypothesis. This highlights the difficulty in predicting profitable strategies ahead of time, emphasizing the unpredictable nature of achieving sustained trading success.

In short, the article concludes that TA does not work, as it fails to identify any technical trading strategies that yield consistent profits across both periods. The results consistently show that apparent profitability often arises from parameter selection rather than genuine market inefficiencies, supporting the efficient market hypothesis.

We welcome this type of research that challenges prevailing beliefs. However, in our opinion:

• The sample size is small, covering only three stocks,
• While the examined period is long, market dynamics may have changed over time. Using an extended dataset is beneficial, but a trading system should account for shifts in market conditions,
• Only two types of tests were conducted; more comprehensive testing is needed.

That said, we look forward to seeing further research in this area. Let us know what you think in the comments below or in the discussion forum.

References

[1] Wang, Y., Chen, Y., Tian, H., & Wayne, Z. (2025). Evaluating Technical Trading Strategies in US Stocks: Insights From Data-Snooping Test. Journal of Accounting and Finance, 25(1).

Article Source Here: Technical Trading Strategies: Profitable or Just Curve-Fitting?

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Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns

The decomposition of the volatility risk premium (VRP) into overnight and intraday components is an active area of research. Most studies indicate that the VRP serves as compensation for investors bearing overnight risks.

Reference [1] continues this line of research, with its main contribution being the decomposition of the variance risk premium into overnight and intraday components using a variance swap approach. The study also tests the predictive ability of these components and examines the seasonality (day-of-week effects) of the VRP.

The authors pointed out,

This paper uses the P&L of a hypothetical variance swap position and breaks down the variance risk premium into its overnight and intraday components to provide some further insights on the nature of the variance risk premium. In the empirical analysis we use implied variance stock indices in US, Europe and Asia and find that the variance risk premium switches sign between overnight and intraday periods. During the overnight period the variance risk premium is negative and during the intraday trading period the variance risk premium becomes positive. Our findings suggest that the negative variance risk premium reported in numerous previous studies is primarily driven by the overnight period variance risk premium component. We also evaluate the ability of the intraday and overnight variance risk premium to predict future equity returns. We find that that the intraday component captures short-term risk and displays predictive ability at 1–3-month horizons, while the overnight component reflects longer term risk and displays predictive ability at 6-12-month horizons.

In summary, the study reaffirms that the variance risk premium is significantly negative during the non-trading overnight period, while it becomes positive and often insignificant during the intraday trading period.

An interesting finding is the day-of-week seasonality. For instance, going long volatility at the open and closing the position at the close tends to be profitable on most days, except Fridays.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Papagelis, Lucas and Dotsis, George, The Variance Risk Premium Over Trading and Non-Trading Periods (2024). https://ift.tt/7zVDyfm

Originally Published Here: Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns

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Using ChatGPT to Extract Market Sentiment for Commodity Trading

A Large Language Model (LLM) is an advanced AI system trained on vast amounts of text data to understand, generate, and analyze human language. In finance, LLMs are used for tasks like analyzing earnings reports, generating market sentiment analysis, automating financial research, and enhancing algorithmic trading strategies. They can process vast amounts of unstructured data, such as news articles, SEC filings, and analyst reports, to extract insights and identify patterns that may impact markets.

In this context, Reference [1] examines the effectiveness of ChatGPT in predicting commodity returns. Specifically, it extracts commodity news information and forecasts commodity futures returns. The study gathers over 2.5 million articles related to the commodity market from nine international newspapers across three countries, covering a diverse set of 18 commodities.

ChatGPT-3.5 is then used to assess the sentiment of these articles by analyzing their headlines, abstracts, or body content, classifying the news as either good or bad for the commodity market. It then constructs the Commodity News Ratio Index (CNRI) and conducts a comprehensive set of tests to evaluate its forecasting efficacy for commodity futures returns.

The authors pointed out,

This paper develops a text‐based CNRI using ChatGPT, derived from a comprehensive analysis of over 2.5 million articles sourced from nine newspapers. Our empirical results demonstrate that the ChatGPT‐based CNRI is significantly effective in forecasting 1‐ to 12‐month accumulated commodity futures index excess returns, as evidenced by both in‐sample and out‐of‐sample regression analyses. Moreover, we control for business variables and economic indicators to examine the significance of predictability.

Our analysis further reveals that the CNRI exhibits enhanced predictive power during periods of economic expansion, contango markets, and declining inflation. We also confirm that our constructed CNRI contains valuable predictive information regarding future macroeconomic performances. In summary, our study illustrates that ChatGPT can effectively predict trends in the commodity market, enhancing our understanding of the information processing capabilities of LLMs and their implications for investors in the financial market.

In short, ChatGPT proves useful in forecasting commodity market dynamics and provides valuable insights for investors and risk managers.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Shen Gao, Shijie Wang, Yuanzhi Wang, Qunzi Zhang, ChatGPT and Commodity Return, Journal of Futures Markets, 2025; 1–15

Post Source Here: Using ChatGPT to Extract Market Sentiment for Commodity Trading

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Market Reactions to Corporate Earnings Under Different Volatility Regimes

Earnings announcements impact a company's stock volatility. Reference [1] examined the earnings reports of Dow Jones Industrial Average components over 10 years to better understand the relationship between different volatility regimes and the time it takes for earnings information to be fully absorbed by the market.

The study focuses on the past ten years, a period that includes three distinct volatility regimes, multiple economic cycles, and shifts from low to rising and ultimately declining interest rates. These evolving market conditions provide a diverse backdrop for assessing how corporate earnings disclosures influence investor behavior and price discovery.

The authors pointed out,

Our analysis as shown in Table 3, confirms the initial hypothesis that the time required for a firm’s earnings or performance news to be fully incorporated into its stock price depends substantially on the prevailing volatility regime, as identified by the SETAR model. Under high-volatility conditions, investors process information more swiftly, leading to shorter impulse response functions (IRFs) being derived from the VAR model. Conversely, information diffuses slower in low-volatility markets, resulting in extended IRFs. While most stocks among the Dow 30 revert to baseline levels within 3–5 days, technology and financial firms often display more drawn-out fluctuations due to their heightened sensitivity to market sentiment and macroeconomic indicators.

…Higher-beta stocks, such as Boeing (BA) and Goldman Sachs (GS), exhibited more significant and persistent price swings, even in conditions where rapid market absorption would otherwise be expected. This phenomenon is especially evident during lower-volatility regimes, suggesting that firm-specific risk factors can prolong the market’s adjustment process despite a relatively subdued overall sentiment.

In short, the time required for earnings news to be fully reflected in stock prices depends on the prevailing volatility regime. In high-volatility markets, investors process information faster, while in low-volatility conditions, information diffuses more slowly. Higher-beta stocks show more persistent price swings, especially in low-volatility environments.

The authors also discuss the policy implications of their findings regarding the frequency of financial reporting,

…frequent reporting can enhance market transparency and amplify short-term volatility and sector-specific risks. Policymakers could exempt specific high-volatility sectors from mandatory frequent reporting to balance transparency with market stability.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Ugras, Y. J., & Ritter, M. A. (2025). Market Reaction to Earnings Announcements Under Different Volatility Regimes. Journal of Risk and Financial Management, 18(1), 19.

Article Source Here: Market Reactions to Corporate Earnings Under Different Volatility Regimes

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Detecting Trends and Risks in Crypto Using the Hurst Exponent

The Hurst exponent is a statistical measure used to assess the long-term memory and persistence of a time series. It quantifies the tendency of a system to revert to the mean, follow a random walk, or exhibit a trending behavior. A Hurst exponent (H) value between 0 and 0.5 indicates mean-reverting behavior, H = 0.5 suggests a purely random process, and H between 0.5 and 1 signals persistent, trending behavior.

In finance, the Hurst exponent is widely used to analyze market efficiency, detect trends, and evaluate the predictability of asset prices. In this context, Reference [1] utilized the Detrended Fluctuation Analysis technique to study the Hurst exponent of the five major cryptocurrencies. Its main novelty is the calculation of a weekly time series of the Hurst exponent and its analysis.

The authors pointed out,

• Firstly, the Hurst exponent (H) can be utilized to monitor trend continuation or reversal. In our analysis, transitions between these regimes were observed in some cryptocurrencies, such as XRP, which displayed short-term persistence followed by long-term anti-persistence. These shifts could potentially serve as early indicators of trend changes. For instance, monitoring rolling-window DFA estimates over time could help identify when a cryptocurrency market transitions from trend-following (H>0.5) to mean-reverting behavior (H<0.5), aiding in dynamic strategy adjustments to enhance decision-making.
• Secondly, the study highlights distinct asset-specific behavioral characteristics across cryptocurrencies. The heterogeneous behaviors observed suggest that H-based analysis could inform tailored trading strategies for different assets…
• Lastly, the observed synchronization in H values across multiple cryptocurrencies during extreme market events offers potential for systemic risk monitoring. For instance, collective shifts to anti-persistent behavior (H<0.5) may signal heightened volatility or market instability, enabling traders to adjust portfolios or implement defensive measures such as diversification.

In short, the findings suggest opportunities for using Hurst exponents as tools to monitor trend continuation or reversal, develop asset-specific strategies, and detect systemic risks during extreme market conditions, offering valuable insights for traders and policymakers navigating the cryptocurrency market's inherent volatility.

This is a useful application of the Hurst exponent, and it is not limited to cryptocurrencies but can be applied to any market.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Huy Quoc Bui, Christophe Schinckus and Hamdan Amer Ali Al-Jaifi, Long-Range Correlations in Cryptocurrency Markets: A Multi-Scale DFA Approach, Physica A: Statistical Mechanics and its Applications, (2025), j.physa.2025.130417

Post Source Here: Detecting Trends and Risks in Crypto Using the Hurst Exponent

source https://harbourfronts.com/detecting-trends-risks-crypto-using-hurst-exponent/

How End-of-Month Returns Predict the Next Month’s Performance

Calendar anomalies in the stock market refer to patterns where stock returns deviate from expected behavior based on the time of year, month, or even day. Well-documented anomalies include the "Sell in May and Go Away" strategy, which suggests that stocks underperform during the summer months, and the "Turn-of-the-Month Effect," where returns are typically higher in the last few days of one month and the first few days of the next.

Reference [1] introduced a novel calendar anomaly known as the end-of-month reversal effect. The study showed that end-of-month returns, i.e. returns from the fourth Friday to the last trading day of the month, are negatively correlated with returns in the following month. The author pointed out,

This paper documents a novel 1-month aggregate market reversal pattern. This pattern is driven by the previous end-of-the-moth market return. The empirical evidence is statistically significant both In- and Out-of-Sample. Importantly, I show that the reversal at the aggregate level has characteristics opposite to those established in the cross-sectional literature: it concentrates on high-priced and liquid stocks and is cyclical with the economy. Consequently, a simple rule of thumb and more sophisticated strategies deliver sizable economic gains.

In short, a simple trading strategy based on this effect, that is buying if the end-of-month return is negative and selling if it is positive, outperforms the buy-and-hold strategy over a 45-year period.

The author also provides an explanation for this anomaly, attributing it to pension funds’ liquidity trading, as they adjust their portfolios to meet pension payment obligations.

I rationalize the empirical findings via pension funds’ end-of-the-month liquidity trading. Leveraging on recent findings and on direct evidence from daily pension funds trading activity, I argue that the payment cycle potentially triggers a non-informational trading shock. Consistent with a payment cycle explanation, I show that the reversal pattern increases in absolute terms within the one month ahead, aligning with the time pension funds receive inflows.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Graziani, Giuliano, Time Series Reversal: An End-of-the-Month Perspective, 2024, https://ift.tt/3Qoxzpe

Post Source Here: How End-of-Month Returns Predict the Next Month’s Performance

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Inventory Risk and Its Impact on the Volatility Risk Premium

The volatility risk premium (VRP) is the difference between the implied volatility of options and the realized volatility of the underlying asset, representing the compensation investors earn for taking on volatility risk.

Recent research suggests that the VRP is specifically a reward for bearing overnight risk. Reference [1] goes further by attempting to answer why this is the case. It provides an explanation in terms of market makers' inventory risks. The authors pointed out,

This paper suggests that S&P 500 option risk premia largely result from the combination of options demand and overnight equity illiquidity, which expose risk-averse intermediaries to unhedgeable inventory risk. I show that S&P 500 option risk premia are on average insignificant intraday, but significantly negative overnight, outside of regular exchange trading hours. Dealers’ inventory exposure to overnight equity price gaps can explain this finding. Dealers have a net-short position in put options, which exposes them to overnight equity “gap risk”, the risk that equity prices change overnight, since overnight equity liquidity is too low for continuous delta-hedging. In contrast, intraday equity liquidity presents few such obstacles. Supporting this channel, the emergence of overnight equity trading around 2006 leads to a relative reduction in option risk premia over parts of the week that include more overnight trading sessions, suggesting a causal effect of equity liquidity on option risk premia, likely through dealers’ inventory risk.

In summary, the article concluded that,

• Put option risk premia are significantly negative overnight when equity exchanges are closed and continuous delta-hedging is not feasible. Intraday, when markets are liquid and delta-hedging is possible, put option risk premia align with the risk-free rate. Call options show no significant risk premia during the sample period.
• Dealers' short positions in puts expose them to overnight equity price "gap" risks, while their call option positions are more balanced between long and short, resulting in minimal exposure to gap risk.
• Increased overnight liquidity reduces option risk premia. Regulatory changes and the acquisition of major electronic communication networks in 2006 boosted overnight equity trade volumes from Monday to Friday, reducing the magnitude of weekday option risk premia compared to weekend risk premia.

An interesting implication of this research is that the introduction of around-the-clock trading could potentially reduce the VRP, as increased liquidity and continuous trading would mitigate overnight risk exposure for market participants.

Let us know what you think in the comments below or in the discussion forum.

References

[1] J Terstegge, Intermediary Option Pricing, 2024, Copenhagen Business School

Originally Published Here: Inventory Risk and Its Impact on the Volatility Risk Premium

source https://harbourfronts.com/inventory-risk-impact-volatility-risk-premium/

Does Trend Following Still Work on Single-Name Stocks? Updated Results

In a paper published in 2005, Wilcox et al. [1] showed that trend following worked on single-name stocks. Twenty years later, they retested the methodology using new, survivorship bias-free data [2].

Basically, the trading system works as follows:

• Entry: If, at the close of day t, a stock meets the price and liquidity filters, and its closing price equals or exceeds the highest adjusted close in its history, a buy order is placed at the open on day t + 1.
• Exit: At the close of day t, a trailing stop level is calculated using the Average True Range (ATR). This trailing stop is updated daily but never lowered. If, at the close of day t, the stock’s price falls below the trailing stop level, a sell order is executed at the open on day t + 1.

The authors pointed out,

This study highlights the sustained potential of long-only trend-following strategies applied to U.S. equities, building on and extending the foundational research of Wilcox and Crittenden [1]. By analyzing over 75 years of data and more than 66,000 trades, the paper confirms the profitability of trend-following systems, driven by a small number of outsized winners that compensate for more frequent, smaller losses. The strategy’s ability to thrive in various market conditions underscores its robustness, even in the face of evolving market dynamics.

In summary, even after 20 years, the original method remains profitable. However, under realistic conditions, transaction costs made it impractical, particularly for small accounts. To address this issue, the authors implemented a cost-saving mechanism to manage transaction costs. As a result, after accounting for transaction costs, small accounts became more profitable.

…While the theoretical model demonstrates exceptional performance, with a compound annual growth rate (CAGR) of 15.02%, an annualized alpha of 6.19%, and a maximum drawdown of 31.75%, the practical implementation of this strategy is challenged by high turnover and transaction costs. These obstacles, particularly impactful for smaller portfolios, were addressed by introducing a Turnover Control mechanism, which significantly enhances cost-efficiency and ensures alignment with theoretical results.

We believe the results are commendable, but we note a highly skewed profit distribution, with less than 7% of trades driving cumulative profitability. This makes it challenging for a small account to select the right stocks to trade profitably.

Let us know what you think in the comments below or in the discussion forum.

References

[1] C. Wilcox, & E. Crittenden, Does Trend-Following Work on Stocks? The Technical Analyst, 14, 1-19, 2005

[2] Zarattini, Carlo and Pagani, Alberto and Wilcox, Cole, Does Trend-Following Still Work on Stocks? 2025. https://ift.tt/nsdToXM

Originally Published Here: Does Trend Following Still Work on Single-Name Stocks? Updated Results

source https://harbourfronts.com/trend-following-still-work-single-name-stocks-updated-results/

Examining ITM Options Among Retail and Professional Traders

A significant amount of research has focused on at-the-money (ATM) and out-of-the-money (OTM) options due to their liquidity and leverage effects. However, little attention has been given to in-the-money (ITM) options.

Reference [1] addresses this gap by studying ITM options, particularly in the context of retail traders. The author pointed out,

This study fills the gap by highlighting the economic significance of ITM options and examining the behavioral and economic factors that influence investor preferences for these lower-leverage instruments. ITM options, particularly those with short maturities, have become increasingly popular with retail investors due to their perceived higher probability of payoff and the potential for consistent, albeit smaller, returns. By constructing one of the most comprehensive open-close option databases, covering 70% of the equity options market, I provide new insights into the trading behaviors of small customers, who drive much of the ITM options activity.

Among the findings, I observe that ITM options capture a significantly larger share of the dollar volume traded by small customers, especially in large-cap stocks and short-term contracts. Retail investors, as evidenced by social media data from StockTwits, are particularly drawn to ITM call options during periods of heightened retail attention, often focusing on high-priced technology stocks. This trend persists even when controlling for stock returns, volatility, and news volume, suggesting that social media plays a critical role in shaping retail trading behavior.

In summary, the key findings are:

• ITM options deliver more stable returns, reinforcing the idea that retail investors are attracted to their higher probability of generating positive returns in short-term strategies.
• The average dollar volume of ITM options exceeds that of OTM options for trades made by small customers. This trend is less noticeable for options traded by professionals and firms.
• Small customers trade a higher dollar volume of ITM options for maturities of less than seven days.
• The dollar volume of ITM call options traded by small customers is predominantly concentrated in large-cap technology stocks.

This is an interesting study. We believe that ITM options can be beneficial not only for retail traders but also for professionals.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Edna Lopez Avila, In the Money? Low-Leverage in the time of Option Betting, 2025, MIT

Originally Published Here: Examining ITM Options Among Retail and Professional Traders

source https://harbourfronts.com/examining-itm-options-among-retail-professional-traders/

Measuring Jump Risks in Short-Dated Option Volatility

Unlike long-dated options, short-dated options incorporate not only diffusive volatility but also jump risks. The commonly used VIX and SKEW indices cannot clearly identify the jump risk component in options volatility. To better isolate and present the jump risk component, Reference [2] developed a stochastic jump volatility model that includes jumps in the underlying asset. The authors pointed out,

In this paper, we have pioneered a methodology to gauge forward-looking crash risk as implied from option prices. Utilizing the tractable SVJ model, this parametric approach isolates the jump size component from the stochastic volatility encapsulated within uncertainty risk. Our method extends beyond the traditional Black-Scholes model, paralleling the construction of the implied volatility surface and facilitating the creation of an option-implied crash-risk curve ...

Our method’s efficacy is underscored by its strong correlation with non-parametric option-implied skewness. Nevertheless, we have crafted our CIX as a nuanced measure of crash risk, designed to adjust for the influence of V_t, and illuminate the tail risk aspects of asset pricing dynamics. In juxtaposition, option-implied skewness is reliant on both crash and stochastic volatility risks and epitomizes the more smooth characteristics of the risk-neutral density.

Empirically, we uncover an intriguing upward trend in CIX following the 2008 financial crisis.This finding is well supported by narratives about rare events in news coverage, highlighting the importance of incorporating beliefs about rare events within a theoretical framework.

In short, the author utilized this new framework and calculated a skew index, referred to as a Crash Index, to represent the jump component. This index is highly correlated with the traditional SKEW index, and they also uncovered an interesting upward trend in the Crash Index following the 2008 financial crisis.

This is not the first paper to address jump risks in short-dated options, but its key contribution lies in the construction of a skew index. To the best of our knowledge, one of the earliest works in this area is by Carr et al. [2]

Let us know what you think in the comments below or in the discussion forum.

References

[1] Gao, Junxiong and Pan, Jun, Option-Implied Crash Index (2024). https://ift.tt/3DKGvnw

[2] P Carr, L Wu, What type of process underlies options? A simple robust test, The Journal of Finance, 2003

Originally Published Here: Measuring Jump Risks in Short-Dated Option Volatility

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