Review of Momentum and Contrarian Approaches in Global Stock Markets

Momentum and mean-reverting approaches are two primary methods for trading linear (delta-one) assets. Their effectiveness depends on several factors, such as time horizon, investor behavior, liquidity, etc. Reference [1] conducted a comprehensive literature review of momentum and mean-reverting approaches. It aims to answer two questions:

• How have momentum and contrarian approaches performed in different stock markets according to past research?
• What factors influence the success or failure of both approaches according to empirical evidence across countries and periods?

The authors pointed out,

This study concludes that momentum and contrarian investment strategies exhibit effectiveness that is highly dependent on market context, investment period, and the behavior and structure of market participants. Momentum strategies are shown to generate abnormal returns in the short term, particularly in markets with low to medium levels of information efficiency. In contrast, contrarian strategies tend to provide more significant returns in the long run, especially in markets dominated by retail investors and prone to overreaction. The results also show that there is no one universally superior strategy, its success is largely determined by the time horizon, market conditions (bullish vs bearish), the risk model used, and the underlying market microstructure. In addition, recent research trends show a shift towards more adaptive and complex strategies, such as volatility-based momentum, switching strategies, and the use of behavioral indicators such as investor attention. However, challenges remain, such as the risk of momentum crashes, transaction costs, and limited generalizability across markets.

In short, the effectiveness of momentum and mean-reverting approaches depends on multiple factors. Investors should adapt them to their specific markets.

The article also suggests future directions for improving these methods.

In addition, the development of strategies based on market regime changes (bullish-bearish) and the integration of alternative data such as social media sentiment or online search could be promising new approaches. Microstructure research is also needed to better understand how momentum and contrarian strategies work in the hands of retail versus institutional investors.

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

References

[1] Wayan Eka Heltyani, Made Surya Negara Sudirman, Effectiveness of Momentum and Contrarian Strategies: A Systematic Literature Review Across Countries, Models, and Market Conditions, The Journal of Financial, Accounting, and Economics Vol. 2, Issue. 2, July (2025), 111- 130

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Delta Hedging with Implied vs. Historical Volatility

Delta hedging is a technique used to reduce the directional risk of an options position. Most research in this area focuses on hedging with Black-Scholes-Merton (BSM) implied volatility or advanced volatility models. Reference [1] compares the performance of delta hedging using implied volatility (IV) versus historical volatility (HV).

The authors pointed out,

This study provides a comprehensive empirical comparison between implied volatility (IV) and historical volatility (HV) within the context of delta-neutral hedging strategies, specifically focusing on short-term options trading of the Nasdaq-100 ETF (QQQ). Through performance evaluation, sensitivity analysis, and hedging error measurement, the research concludes that IV-based hedging strategies offer superior performance in terms of stability, responsiveness, and risk management accuracy. IV-based strategies, due to their forward-looking nature and market-derived inputs, enable more accurate delta calculations and reduce rebalancing frequency, ultimately minimizing transaction costs and hedging mismatches. These characteristics make IV-based strategies particularly suitable for risk-averse investors and institutional trading environments that demand high precision and efficiency. In contrast, HV-based strategies, although simpler and easier to implement, suffer from lagging responsiveness during periods of market volatility, leading to larger hedging errors and higher portfolio return variance. Nevertheless, HV-based models may still hold value in stable or long-horizon scenarios where historical price trends are more informative.

In short, delta hedging with implied volatility offers more advantages compared with hedging using historical volatility.

This is an important line of research, though the paper has several limitations, notably the small sample size and the lack of clarity on the specific delta hedging strategy employed. Nonetheless, this direction is worth pursuing, particularly in establishing a delta band and determining the optimal hedging frequency.

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

References

[1] Yimao Zhao, Implied Volatility vs. Historical Volatility: Evaluating the Effectiveness of Delta-Neutral Hedging Strategies, Proceedings of the 2025 5th International Conference on Enterprise Management and Economic Development (ICEMED 2025)

Originally Published Here: Delta Hedging with Implied vs. Historical Volatility

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Profitability of ETF Pairs Trading

Pairs trading is a market-neutral strategy that exploits temporary deviations in the price relationship between two historically correlated or cointegrated assets by going long the undervalued asset and short the overvalued asset, aiming to profit from spread mean reversion.

There is an emerging study in the literature that highlights the diminishing profitability of pairs trading. Reference [1] revisits this subject. It studied ETF pairs, using cointegration on the pair ratio as the criterion for evaluating pairs.

The authors pointed out,

The study confirms that the effectiveness of pairs trading is heavily contingent upon the stability and persistence of cointegration relationships between asset pairs. Notably, lowering the z-score threshold from 2 to 1.5 revealed more trading opportunities and improved total profits, but the strategies still faced significant challenges due to short trading windows and increased volatility. These findings are consistent with prior research, such as Do and Faff (2010) Rad et al. (2016), which suggest that the profitability of pairs trading has diminished over time due to increased market complexities and risks.

The effectiveness of ETF pairs trading strategies is highly sensitive to prevailing market conditions, which can significantly impact profitability and risk. Periods of heightened volatility, such as during financial crises or geopolitical uncertainty, can disrupt mean-reverting relationships, causing pairs to diverge for extended periods instead of reverting.

Potential improvements to the pairs trading strategy include the development of adaptive thresholds that adjust based on market volatility or other indicators, enhancing the strategy's robustness in different market environments. Integrating fundamental analysis with cointegration testing could help identify more stable and profitable pairs, improving the strategy's long-term viability.

This paper investigates an important issue that is not often discussed in pairs trading literature: the stability of cointegration. If it fails, then the pairs would no longer be profitable.

The paper presented several important conclusions:

• Lowering the z-score increases profitability but also raises P&L volatility and leads to deeper drawdowns, though recovery tends to be quick.
• Filtering pairs based on p-value improves performance.
• Using the VIX as a regime filter is effective.
• The study also recommends using the price ratio and testing for stationarity.

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

References

[1] Kezhong Chen, Constantinos Alexiou, Cointegration‑based pairs trading: identifying and exploiting similar exchange‑traded funds, J Asset Manag (2025)

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Improving Crypto Volatility Forecasts with Sentiment Data

Accurately forecasting volatility is essential in portfolio and risk management. Typically, volatility forecasting is performed using econometric models such as GARCH and ARIMA. These methods can also be applied to cryptocurrencies. However, a unique feature of cryptocurrencies is their higher susceptibility to market sentiment. We have discussed their sensitivity to sentiment in a previous post.

Reference [1] proposed combining traditional econometric models with sentiment analysis to forecast volatility. Specifically, it utilized RoBERTa to analyze social media and news sentiment, then developed three hybrid models for volatility forecasting. These models are,

• Sentiment-Augmented ARIMA: integrated SNR-filtered sentiment as an external variable, enabling forecasts to adjust dynamically to shifts in market mood.
• Attention-LSTM: combined past log-returns and sentiment vectors in an LSTM with self-attention to focus on periods when sentiment peaks were most predictive.
• Transformer Fusion: used multi-head self-attention on interleaved price and sentiment embeddings to capture long-range dependencies and cross-modal interactions without recurrence.

The authors pointed out,

This study demonstrates the significant benefits of integrating high‑quality sentiment signals with advanced time series models to forecast short‑term Bitcoin volatility. By introducing a robust signal‑to‑noise filtration mechanism, we were able to isolate meaningful market sentiment from the ubiquitous noise of social media chatter and news feeds. Our empirical evaluation, spanning classical ARIMA, standalone LSTM, attention‑based recurrent architectures, and a transformer‑based fusion model, clearly shows that hybrid designs outperform single‑modality approaches across error and directional accuracy metrics. In particular, the Transformer Fusion model achieved the lowest forecasting errors and highest directional reliability, while the Attention‑LSTM provided a compelling balance between performance and interpretability for real‑time applications. Beyond raw predictive gains, this framework offers actionable insights into the temporal dynamics of sentiment and volatility. Cross‑correlation analyses confirmed that filtered sentiment leads to volatility spikes by several intervals, validating its role as an early warning indicator. Moreover, the attention weights and SHAP‑inspired interpretability afford practitioners visibility into which historical windows and sentiment bursts drive model forecasts, addressing the “black‑box” concerns often associated with deep learning in financial contexts.

In short, incorporating sentiment into econometric models improves their predictive power. An interesting finding of the research is the volatility clustering of cryptocurrencies, where Bitcoin log-returns show that high-volatility periods tend to follow high-volatility periods, and calm periods follow calm periods, supporting the use of recent volatility in prediction models.

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

References

[1] MK Rahman, A Raheem, Crypto Noise vs. Signal: A Hybrid Sentiment-Time Series Framework for Predicting Short-Term Bitcoin Volatility, Global Knowledge Academy, Volume-VI, Issue-III (2025)

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Reducing Transaction Costs in Volatility-Managed Portfolios

Volatility targeting is a risk and portfolio management technique that adjusts exposure based on changes in asset volatility. We have discussed volatility targeting methods in previous posts, for example, Applying Volatility Management Across Industries. While effective, this technique requires frequent rebalancing. As with any approach involving high turnover, such as delta hedging, transaction costs can significantly erode performance.

Reference [1] investigates the volatility targeting technique under the constraint of transaction costs. Specifically, it introduces a rebalancing boundary, which triggers adjustments only when certain conditions are met. In other words, the target volatility portfolio E, with the rebalancing boundary, modifies its asset allocation only when the ratio of target volatility to asset volatility deviates from the desired ratio by at least a predefined threshold.

The authors pointed out,

To reduce the transaction costs that a target volatility strategy may encounter over an investment horizon, we incorporate a novel rebalancing boundary setup into the volatility target asset allocation mechanism. We identify the optimal rebalancing boundary level that maximizes a portfolio return measure while controlling the portfolio’s risk measure within a constrained optimization setting, focusing on the Omega ratio and mean volatility deviation as the portfolio return and risk measures, respectively. We evaluate the performance of the target volatility portfolio with the optimal rebalancing boundary (“Optimized Portfolio E”) and contrast it with a traditional target volatility portfolio without such rebalancing boundaries (“Portfolio S”) by conducting comparative analysis under different market scenarios. We find that the optimized Portfolio E contains significantly lower transaction costs while generating higher returns than the benchmark Portfolio S. Moreover, the optimized Portfolio E realizes lower portfolio downside risk, as indicated by a higher level of Value-at-Risk (VAR) than Portfolio S. This suggests that our proposed enhancement to the target volatility strategy by adding the rebalancing boundary level does not compromise portfolio risk control.

In short, using a rebalancing boundary reduces transaction costs and improves the portfolio’s risk-adjusted returns.

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

References

[1] Zefeng Bai, Dessislava Pachamanova, Victoria Steblovskaya, Kai Wallbaum, Target volatility strategies: optimal rebalancing boundary for transaction cost minimization, Financial Markets and Portfolio Management, 2025

Article Source Here: Reducing Transaction Costs in Volatility-Managed Portfolios

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Jumps and Volatility Clustering in AI-Driven Markets

AI-assisted trading is a growing area in quantitative finance. However, concerns have emerged that it may destabilize markets. We recently discussed how trading strategies generated by large language models could introduce new systemic risks to financial markets.

Continuing this line of research, Reference [1] examines how AI trading affects market volatility, liquidity, and systemic risk. The authors used daily data from the S&P 500 index, applying an OLS regression and a Poisson model to estimate the frequency of extreme price jumps, and a GARCH(1,1) model to analyze volatility clustering. They pointed out,

One of the key takeaways is the lack of a strong direct relationship between AI trading and market fluctuations. While AI trading does not appear to significantly drive volatility under normal conditions, its effects may depend on broader market structures, liquidity availability, and macroeconomic shocks. This suggests that AI-based trading systems do not inherently destabilize markets but may interact with other variables in ways that influence financial stability. In periods of normal trading activity, AI may enhance price efficiency and liquidity provision. However, during financial distress or economic uncertainty, algorithmic decision-making could amplify volatility through feedback loops and self-reinforcing mechanisms. The persistence of volatility observed in the GARCH model supports this argument, indicating that once volatility spikes occur, they tend to last longer.

A significant finding of this study is the strong correlation between energy consumption and market volatility. Unlike AI Presence, energy consumption emerges as a key driver of financial fluctuations. The high computational demands of AI trading suggest that energy-intensive models may contribute to extended periods of heightened volatility, raising concerns about both financial stability and sustainability.

In short, the results show that AI-driven trading is positively associated with more frequent market jumps and higher volatility. Interestingly, AI does not impact markets directly, but rather through the energy consumption used to train the models.

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

References

[1] Zorina ALLIATA, Andreea-Mădălina BOZAGIU, The Impact of AI on Market Volatility: A Multi-Method Analysis Using OLS, Poisson, and GARCH Models, Proceedings of the 19th International Conference on Business Excellence 2025

Article Source Here: Jumps and Volatility Clustering in AI-Driven Markets

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Analyzing Crypto Market Sentiment with Natural Language Processing

Sentiment analysis is a growing research area in quantitative finance, especially with the advancement of Large Language Models (LLMs) and Natural Language Processing (NLP). Sentiment-based trading plays an important role in traditional finance; however, it is even more relevant in DeFi and crypto markets, which are known for their volatility and are significantly influenced by investor sentiment.

Reference [1] utilizes NLP to analyze sentiment in the crypto market and its impact on cryptocurrencies. Specifically, it investigates the relationship between sentiment in financial news articles and cryptocurrency price movements using natural language processing and statistical correlation analysis. The study consists of three phases: sentiment extraction, sentiment clustering, and correlation analysis.

The authors pointed out,

The study finds that Ethereum (ETH) has the strongest correlation between sentiment and price trends, with the hybrid sentiment correlation increasing from 0.3819 to 0.3900 over 24 hours…One possible explanation is Ethereum’s deep integration with DeFi applications and smart contracts, where sentiment-driven narratives (such as network upgrades, ecosystem developments, and new partnerships) significantly impact investor decisions.

Bitcoin exhibits a moderate sentiment correlation (0.2899 hybrid), which slightly increases after 12-24 hours. This suggests that while Bitcoin is affected by sentiment, other macroeconomic factors, institutional trading behaviors, and regulatory developments have a greater influence on its price.

Unlike Ethereum, Bitcoin has established itself as “digital gold” and a macroeconomic hedge, attracting a higher proportion of institutional investors, hedge funds, and long-term holders who base their decisions on fundamental and technical factors rather than short-term sentiment shifts. This makes BTC’s price less reactive to immediate sentiment fluctuations compared to ETH.

XRP demonstrates the weakest correlation with sentiment (0.1005 hybrid, increasing slightly to 0.1205 after 24 hours), suggesting that its price movements are largely disconnected from sentiment-based trading. A major reason for this weak correlation is XRP’s centralized development structure and reliance on partnerships with financial institutions.

In short, the article concluded that ETH shows the strongest sentiment-price correlation, BTC is moderately influenced, and XRP is the least impacted. Traders react to sentiment with a 12–24 hour lag, creating opportunities for predictive trading strategies.

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

References

[1] Franco Farrugia, Cedric Deguara, Sentiment Analysis and Cryptocurrency Price Correlation: A Data-Driven Study, MCAST Journal of Applied Research & Practice 2025; 9 (2) : 166-184

Post Source Here: Analyzing Crypto Market Sentiment with Natural Language Processing

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Decomposing the Variance Risk Premium: Up and Down VRP

The variance risk premium (VRP) is a well-researched topic in quantitative finance. The VRP is the difference between the market-implied variance and the expected realized variance of an asset. Usually, the VRP is positive, reflecting the compensation investors demand for bearing volatility risks. Reference [1] extends this analysis further and breaks down the VRP into two components: an up (UVRP) and a down (DVRP) component.

The authors pointed out,

…we dissect the VRP in terms of upside (UVRP) and downside (DVRP) variance risk premia. The DVRP is the main component of the VRP, and the most important to assess, since investors tend to hedge against downward movements to avoid losing money. Conversely, investors often gravitate toward upside movements and are willing to pay to get exposure to it and the potential for higher profits.

…Thus, the DVRP should be positive (reflecting the compensation required by an agent to bear the downside risk), whereas the UVRP should be negative (viewed as the discount given by an agent to secure a positive return on an investment).

An interesting byproduct of this decomposition is the skewness risk premium, or SRP (simply defined as SRP = UVRP-DVRP), which will be negatively valued by construction. Kozhan, Neuberger, and Schneider (2014) show that compensation for variance and skewness risks are tightly linked.

In addition, this work explores the link between the DVRP and the equity risk premium, or ERP. Current asset pricing research considers that, over shorter time horizons, the VRP provides superior forecasts for the ERP; these periods are less than a year, typically one quarter, ahead (Bollerslev, Tauchen, and Zhou, 2009). To further this exploration, the study also considers the link between the SRP and the ERP at various prediction steps.

In short, the article concluded that,

• DVRP is typically positive, reflecting the premium required to bear downside risk, while the UVRP is typically negative, reflecting a discount for access to upside potential.
• The difference between UVRP and DVRP defines the skewness risk premium (SRP), which is inherently negative and reflects investor preference asymmetry.
• DVRP correlates with the equity risk premium (ERP), especially over short-term horizons such as quarterly forecasts.

This article provides further insights into volatility dynamics. Let us know what you think in the comments below or in the discussion forum.

References

[1] B Feunou, MR Jahan-Parvar, C Okou, Downside variance risk premium, Journal of Financial Econometrics 16 (3), 341-383

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Can AI Trade? Modeling Investors with Large Language Models

Large language models (LLMs) are advanced artificial intelligence systems trained on vast amounts of text data to understand and generate human-like language. LLMs can perform a wide range of language tasks, including translation, summarization, question answering, and code generation. Their versatility has made them valuable tools across industries, from finance and healthcare to education and software development.

Reference [1] utilized LLMs to construct trading agents in the financial markets. Specifically, the author used LLMs to emulate various types of investors: value investors, momentum traders, market makers, retail traders, etc. The article pointed out,

First, LLMs can effectively execute trading strategies. They consistently understand market mechanics, process market information, form price expectations, and execute trades according to specific instructions. Their trading behavior is highly sensitive to the prompts they receive—they faithfully follow directions regardless of profit implications…

Second, LLMs react meaningfully to market dynamics. They consider current and historical prices, dividends, and other market information when making decisions. …

Third, market dynamics with LLM agents can resemble actual markets and mirror classic results from the theoretical finance literature. When these agents interact, they produce realistic price discovery and liquidity provision with emergent behaviors, including price convergence toward fundamental values…

These findings carry significant implications for market structure and regulation. While LLM agents can enhance price discovery and liquidity, their adherence to programmed strategies, even potentially flawed ones derived from prompts, could amplify market volatility or introduce novel systemic risks, as observed in our simulated bubble scenarios. A key concern is the potential for widespread correlated behavior: similar underlying LLM architectures responding uniformly to comparable prompts or market signals could inadvertently create destabilizing trading patterns without explicit coordination. This underscores the critical need for rigorous testing and validation of LLM-based trading systems prior to live deployment.

In short, the article concluded that trading strategies generated by large language models are effective, but could introduce new systemic risks to financial markets because these agents would act in a correlated manner.

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

References

[1] Alejandro Lopez-Lira, Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations, arXiv:2504.10789

Article Source Here: Can AI Trade? Modeling Investors with Large Language Models

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Improving Hedging with Skew-Adjusted Delta

Delta hedging is a method used to reduce or eliminate the directional risk of an options position. In most delta hedging schemes, delta is calculated using the Black-Scholes-Merton (BSM) model. However, the BSM delta is not always accurate due to the assumptions embedded in the model. For a more accurate hedge, adjustments need to be made. We have discussed such an adjustment proposed by Hull and White.

Following this line of research, Reference [1] examined the use of skew-adjusted delta for hedging. The paper retested the method developed by Vähämaa [2] and applied it to S&P 500 index options during the COVID-19 pandemic. Specifically, the author modified the BSM delta as follows,

The author pointed out,

Considering the second hypothesis (H2), the performance of the smile-adjusted delta was retested with data from the S&P 500 index. Both models’ quantitative fit is documented to be approximately 90%, but SAD explains daily option price movements slightly better than the BS delta, regardless of the moneyness and maturity of the option. Indicating, it is reliable to finally compare the delta hedging performance of the two deltas.

The overall conclusion is that SAD outperforms the BS delta in delta hedging, meaning that the second hypothesis (H2) is also true. To contradict Vähämaa’s (2004) results, the empirical finding from delta hedging indicates that SAD outperforms the BS delta most distinctly among ITM options. When the hedging horizon is longer than a day, SAD outperforms the BS delta regardless of the moneyness and maturity of the option. That out-performance becomes even more notable as the hedging horizon lengthens. This out-performance increase is even more distinct than what Vähämaa (2004) documented. This may be due to the relatively steep volatility smile making the smile-adjustment term, or specifically considering the slope of the smile, more crucial.

In short, the article shows that skew-adjusted delta (SAD) performs better than BSM delta, especially for in-the-money options. When the hedge lasts more than one day, SAD outperforms BSM delta across all moneyness and maturities, with the advantage growing as the hedging period gets longer.

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

References

[1] Berg, Wille, From the inaccurate Black-Scholes model to more efficient delta hedging with smile-adjusted extension,  University of VAASA, 2025

[2] Vähämaa, S., Delta hedging with the smile, Financial Markets and Portfolio Management, 18(3), 241-255, 2004

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