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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