Machine Learning and Macroeconomic Indicators for Stock Return Prediction

Financial markets are nonstationary, and predicting asset prices is a nontrivial task. In the past, practitioners often relied primarily on technical indicators, which are derived from prices and trading volume, to forecast asset prices.

More recently, research has increasingly incorporated additional exogenous variables that may contain predictive information. Reference [1] follows this direction by combining technical indicators with volatility measures and macroeconomic variables using deep-learning models and hybrid feature-selection methods.

The authors forecast next-day individual-stock log returns rather than price levels. If the predicted next-day return is positive, the strategy holds the stock; otherwise, it remains out of the market. The study uses daily data from January 2005 to May 30, 2024 for AAPL, XOM, Goldman Sachs, Pfizer, and Ford, selected to represent different industries and company sizes and to cover both the Global Financial Crisis and COVID-19 periods.

In addition to traditional technical indicators, the authors incorporate the VIX, DXY, gold, oil, the 13-week Treasury rate, 10-year Treasury yield, term spread, and dividend yield.  They pointed out,

Identifying the most significant features to enhance stock price prediction has become increasingly complex and challenging. In this study, we demonstrated the impact of using the complete feature set on model performance… The results reveal that combining the filter and wrapper FS methods with DL models yields outstanding results and significantly improves the trading profitability across all the models and stocks.

… The results of this study demonstrate an important and consistent finding: low forecast error does not necessarily improve trading profitability. Empirical results revealed that although LR and RF achieved the lowest RMSE and MAE performance, Bi-GRU consistently produced the highest economic returns. These results not only question the practice of using statistical accuracy (MSE, RMSE, and MAE) as the main criterion for model evaluation but also highlight economic significance analysis, such as trading returns and Sharpe ratios, as a crucial and complementary performance evaluation framework.

This study not only highlights the benefits of the proposed models in enhancing stock price prediction for understanding the behavior of different stock markets, providing significant insights for investors and financial analysts, but also demonstrates that integrating different features with hybrid FS methods and DL models provides a robust framework for forecasting stock closing prices, improving performance and reliability in predicting future stock prices.

In short, the results indicate that incorporating macroeconomic and volatility indicators adds predictive value.

Another important finding of the paper is that forecast-error metrics and trading performance can give very different model rankings; therefore, financial forecasting should evaluate economic outcomes (return/Sharpe) alongside RMSE/MAE, rather than treating statistical forecast accuracy as sufficient.

This is an important contribution, as it provides additional evidence that exogenous variables can improve return forecasting and that machine-learning techniques can further enhance predictive performance.

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

References

[1]  Nabil, Barakat, Aboelfetouh & Elhishi, Integrating macroeconomic and technical indicators into forecasting the stock market: a hybrid approach for efficient feature selection, Scientific Reports 16, 27553 (2026)

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Two Decades of Leveraged ETFs

Leveraged exchange-traded funds (LETFs) first appeared in 2006 and were initially viewed as revolutionary investment products. In Reference [1], the authors examined several important aspects of LETFs. Almost two decades later, following numerous market stress events, including the recent 2026 South Korea extreme-volatility episode, they published a follow-up article [2] revisiting the insights and conclusions of their earlier work.

The authors pointed out,

However, to some observers, these products are characterized by issues often cited as contributors to the global financial crisis including (1) inadequate investor education regarding product complexity, (2) systemic risk arising from rebalancing dynamics, and (3) lack of transparency around hidden frictions….

On the second point, there is now less concern about same-direction rebalancing on the broader market in the US given the relatively small footprint of LETPs relative to total AUM, less than 1%. We do note that LETPs have a much larger share of dollar trading volume, some 16%, and their AUM when factoring leverage is actually understated. A major concern lies with high leverage products on single stocks and niche indexes, where history suggests that value can be quickly destroyed in extreme events as in South Korea in July 2026…

Our formal model twenty years ago addressed key questions raised by investors, market practitioners, regulators, policymakers, and the press seeking to understand a new product. In doing so, it demonstrated the value of the scientific approach to market analysis championed by JOIM. This remains an active area of theoretical and empirical research. Important questions about path-dependent returns, volatility, and microstructure effects remain open and relevant to practitioners. Recent events, including South Korea’s market volatility in the summer of 2026, show that these concerns are not merely theoretical; they can have real consequences for markets and everyday investors.

Basically, the core insights from the original paper [1] still hold: LETPs are useful short-term trading tools, but long-horizon returns are path-dependent, daily rebalancing can amplify market moves, and hidden financing frictions can materially erode returns.

In addition, the paper also finds that

• Predictable rebalancing can act as a momentum accelerator near the close. The effect should be stronger in thin markets, highly leveraged products, and products with large AUM,
• LETPs should primarily be treated as tactical, short-term instruments, not simple long-term leveraged holdings.

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

References

[1] Cheng, M. and A. Madhavan (2009), The Dynamics of Leveraged and Inverse Exchange-Traded Funds, Journal of Investment Management, 7(4), 43–62

[2] Cheng, M., & Madhavan, A. (2026), Twenty Years of Leveraged and Inverse Exchange-Traded Products: What Have We Learned? Working paper

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Tail-Risk Hedging with Deep Out-of-the-Money Puts

Tail-risk hedging is an important topic that has attracted considerable attention in the academic community. However, a fully satisfactory solution has yet to emerge. The main challenge is that hedging comes at a cost, creating a persistent drag on portfolio performance.

Reference [1] addresses the same issue of tail-risk hedging but examines whether using deep out-of-the-money (DOOM) puts can provide a more efficient approach. The authors consider a one-year long SPX exposure combined with European SPX puts, with option spending capped at 5% of initial wealth. Purchasing puts reduces the initial SPX allocation rather than introducing additional leverage.

They apply three pricing models: Black-Scholes-Merton, Merton jump-diffusion, and Heston stochastic volatility models, to calculate risk-neutral exercise probabilities, which are then used to select put-option strikes.

The authors pointed out,

The research question addressed by this thesis was: to what extent can deep out-of-the-money put options enhance downside protection for equity portfolios while preserving cost efficiency under different market regimes? The results show that DOOM puts can reduce downside losses, but only when the selected strike region matches the market regime. This alignment depends on the volatility level and downside skew priced in the option surface. These two factors determine the cost of protection across strikes. The hedge is cost-efficient when the selected strike is close enough to the loss region implied by the market regime, ensuring a sufficiently high probability of finishing in the money relative to the premium paid… The historical SPX backtest reveals that the best protection does not necessarily come from the deepest puts. Deep low-strike puts create a far-tail floor, but they protect only if losses reach that region. In some regimes, a higher-strike put bought in smaller quantity is more robust because it is closer to the relevant loss region and pays in a wider range of realized losses.

In short, the paper finds that DOOM puts can reduce downside losses, but their effectiveness is highly regime-, strike-, surface-, and model-dependent. The deepest puts are not automatically the most cost-efficient. Successful protection requires the strike region to line up with the losses actually being targeted and with the option-surface regime prevailing when protection is purchased.

The results are relatively intuitive to practitioners, and no precise optimization algorithm is presented. Overall, the paper highlights that considerable research is still needed in the area of tail-risk hedging.

An innovative proposal of this paper is the use of risk-neutral tail probabilities to select strikes instead of using moneyness. In the BSM framework, this would correspond to using delta for strike selection, an approach that has been employed by practitioners.

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

References

[1] Roba, A. (2026). To what extent can deep out-of-the-money put options enhance downside protection for equity portfolios while preserving cost efficiency under different market regimes? Master’s dissertation, Louvain School of Management, UCLouvain.

Article Source Here: Tail-Risk Hedging with Deep Out-of-the-Money Puts

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Dollar-Cost Averaging or Buy the Dip?

Dollar-cost averaging (DCA) is an investment approach in which a fixed amount of capital is invested at regular intervals, regardless of market conditions.

Reference [1] examines an interesting problem: suppose you have a steady stream of relatively small cash flows, such as a paycheque. Should you practice DCA, or set aside some or all of the cash and wait for a market dip?

To answer this question, the author utilizes daily VFINX total returns from January 1990 to August 2026 and 440 monthly contributions to model DCA, while evaluating 115 mechanical dip-buying rules as alternatives to immediate investment, that is, different forms of market timing.

The paper  pointed out,

No tradable cash-reserve overlay beats monthly DCA by an economically meaningful margin. Rules that wait for 10–20% peak drawdowns lose 3–17% of terminal wealth. A contribution-level test shows why: a dollar delayed until a 10% drawdown is invested, on average, at a 22% higher total-return price, not a lower one, because delay buys later in a rising market. An oracle that buys exact troughs of every ≥10% episode still loses over the full sample. The same conclusion holds for a separate “dip sleeve” of extra capital: the fair twin is to DCA that extra, and crash sleeves capture only 66–79% of the incremental wealth. For a pile already in hand, lump-sum investment dominates both DCA of the pile and waiting for a dip.

In short, the paper finds that, although some exceptions exist, for a long-horizon S&P 500 saver whose cash arrives over time, the historical evidence favors investing as the cash arrives rather than withholding it for a future dip.  The reason is that with a positive equity premium, delaying investment has an opportunity cost. A future 10% drawdown can occur at a price higher than today’s price because the market may rise substantially before falling.

This problem is also relevant to active traders. Suppose you receive additional capital from investors or accumulate capital gains, and assume that your trading strategies have positive expectancy. Should you immediately increase your allocation, or hold some of the capital in reserve and wait for a drawdown before deploying it?

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

References

[1] Dong, E. K. (2026). Does “Buy the Dip” Improve Dollar-Cost Averaging? Evidence from Fair-Capital Experiments in the S&P 500, 1990–2026. Working paper

Article Source Here: Dollar-Cost Averaging or Buy the Dip?

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Generating Multivariate Synthetic Data for Asset Prices

Backtesting is a necessary step in strategy development, but it is not sufficient to establish that a strategy is robust. A rigorous validation process is also required.

One inherent limitation of conventional backtesting is that it evaluates a strategy against a single realized historical price path, providing no information about how the strategy might have performed under alternative market trajectories. Techniques such as bootstrapping can help address this limitation by generating alternative price paths. Along this line, Reference [1] proposes a new method for generating realistic synthetic multivariate price paths.

The authors use daily prices and returns for 330 stocks that were members of the S&P 500 at some point between January 2000 and April 2016. To assess whether the synthetic data realistically reproduce the statistical characteristics of financial markets, they examine a broad range of stylized facts and multivariate properties, including fat tails/kurtosis, skewness, autocorrelation, volatility clustering, trend properties, cross-asset correlations, time-varying correlations, and directional similarity among assets.

The authors pointed out,

In this work we have presented an approach to simulate virtual scenarios of multivariate financial data as long as desired. This approach generates artificial asset returns that behave much like the real ones do, as measured by our sanity-check constraints. Virtual scenarios can be simulated, at a low computational cost, involving decades of trading days for hundreds of assets within a given market, and even new artificial assets for that market can be created.

First, in order to define the constraints that artificial asset returns/prices must comply with, the best-known stylized facts have been described and some other properties that account for the cross-asset relationship within a specific market have been introduced. Then, some of the most common approaches found in publicly available toolboxes have been tested in order to check how the simulated asset returns/prices reproduce the observed properties…

On the other hand, our proposed approach allows to generate financial datasets as large as desired while still reproducing volatility clustering and cross-assets relationships (and their changes over time) to a great extent, leading to sets of assets that behave as belonging to the same market…

Basically, their approach is analysis-by-synthesis. First, the authors divide the historical market into upward/downward trends using an equally weighted market index. Within each trend, they estimate time-varying multivariate means and covariance matrices using short rolling windows.

They then generate a stochastic sequence of alternating bull/bear trends and draw returns from the corresponding sequence of time-varying multivariate Gaussian distributions. The generated markets reproduce many characteristics of the real market reasonably closely.

This work makes an important contribution to the growing body of research on synthetic financial data and provides another potentially useful tool for rigorous trading-strategy validation.

Another interesting result of the paper is the use of PCA in reverse. Instead of reducing dimensionality, the authors generate new eigenvectors and project the existing principal components back into asset space, thus creating synthetic stock prices.

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

References

[1] Franco-Pedroso, J., Gonzalez-Rodriguez, J., Cubero, J., Planas, M., Cobo, R., & Pablos, F. (2018), Generating virtual scenarios of multivariate financial data for quantitative trading applications. arXiv:1802.01861

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Market Timing Using Option-Implied Distribution

Option prices contain useful information about the underlying asset. The most well-known quantity that can be extracted from option prices is implied volatility, which can be used for various purposes in portfolio and risk management.

Reference [1] goes further and proposes that the probability density distribution extracted from option prices can be used for market timing and portfolio construction. Specifically, the authors utilize 6,540,879 option-implied volatilities from 2018–2023, together with daily index prices for seven indices, the S&P 500, Hang Seng, Euro Stoxx 50, FTSE 100, DAX, CAC 40 and Nikkei 225, to calculate so-called state price densities (SPDs). They then use these densities to construct two market-timing and two portfolio-selection strategies.

The authors pointed out,

Indeed, this work aims to analyze whether some hidden information in the SPD distribution can be used to define a good trading strategy. If so, the advantage of using the SPD would be evident, since it can be easily estimated from option prices, while the P-distribution is difficult to obtain in practice.

To summarize, the aim of this paper is twofold:

• To propose a market timing strategy for a risk-neutral investor when the investment decision focuses on a single index;
• To propose a short-term portfolio selection strategy for a risk-neutral investor when the investment universe is composed of a set of indexes.

… The results show that the proposed market timing strategy nearly always performs better than the simple buy-and-hold strategy and, in multiple cases, our criteria suggest portfolios that beat the 1/N portfolio that we take as a benchmark.

In short, the paper concludes that the shape of the option-implied risk-neutral distribution contains useful short-term directional and cross-sectional information, and that trading strategies based on this information generate better risk-adjusted returns.

Although the paper has some limitations, notably that transaction costs and fully realistic execution are not incorporated, it still provides an interesting framework for extracting and using information embedded in option prices.

Another noteworthy aspect is that the SPD is risk-neutral, meaning that it reflects a risk-neutral investor perspective. There is no need to convert it to a physical probability distribution, suggesting that risk-neutral information can also be useful for decision-making in the physical world.

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

References

[1] Gubareva, M., Kopa, M., & Vitali, S. (2026), Option-implied information for market timing and portfolio selection, Annals of Operations Research.

Post Source Here: Market Timing Using Option-Implied Distribution

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The Performance of Subscription-Based Option Recommendations

Retail options trading volume has increased significantly, attracting growing attention from both market practitioners and academics. We have previously discussed how retail options trading is changing volatility dynamics.

Along the same line, Reference [1] studies this issue but focuses on a small subset of retail options traders. Specifically, the authors examine paid Discord option-trading services, including their performance, publishers' behavior and incentives, and market impact.

To this end, they utilize 10,793 option recommendations from 15 paid Discord servers, covering March 2020–December 2025, matched to OPRA option quotes and trades. The authors also construct a Speculative Index combining leverage, low option premiums, short maturity, small underlying market capitalization, and low underlying stock prices. They pointed out,

Discord option callouts are concentrated in highly speculative contracts, and retail traders respond strongly and quickly to these recommendations. Retail buying begins within seconds of publication and reverses following subsequent trim and exit messages, indicating that subscribers closely follow both entry and trade-management guidance. Although option prices initially rise following callouts, these gains are short-lived. After accounting for realistic execution timing and transaction costs, subscriber returns are significantly negative under economically plausible trading strategies. Under our dynamic 60-minute exit strategy, estimated realized losses from abnormal retail trading in the first five minutes after callouts are approximately $58 million.

The cross-sectional results point to an engagement-performance tradeoff. More speculative recommendations and recommendations with wider bid-ask spreads attract substantially greater retail participation, yet both are associated with weaker realized subscriber returns. Moreover, these patterns extend beyond individual recommendations. Servers on Discord exhibit persistent recommendation styles, and those that consistently feature more speculative or higher-spread contracts attract stronger subscriber engagement but deliver poorer realized investment outcomes.

In short, the paper finds strong evidence that paid Discord option alerts move retail order flow and temporarily move option prices, but ordinary followers generally lose money after realistic execution costs.

Another interesting finding of the paper is the engagement-performance tradeoff: that is, the recommendations attracting the strongest subscriber response systematically produce the weakest realized subscriber outcomes.

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

References

[1] Green, T. C., Jame, R., Oliphant, P., & Roseman, B. S. (2026), Speculation by Subscription: Finfluencers and Retail Option Trading, July 2026.

Article Source Here: The Performance of Subscription-Based Option Recommendations

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When Trading Strategies Look Too Good

Designing a robust trading strategy is not a trivial task. Many systems look good at the design stage but break down or experience deteriorating performance once deployed. There is a small but growing body of research addressing the issue of system robustness. For example, we recently discussed why system performance tends to decay after implementation.

Reference [1] continues this line of research. Specifically, the authors demonstrate how fragile a trading system can be. For this goal, they study a volatility-timing strategy on the S&P 500, DAX, and Nikkei 225 from 1996–2025, where the portfolio is either fully invested or fully out depending on discrete historical-volatility ranges.

The authors pointed out,

Further examination identified several methodological limitations that affected the interpretation of the results. Specifically, the inclusion of contemporaneous information in the t-0 specification of the model introduced look-ahead bias into the strategy. In addition, extensive in-sample parameter optimization together with the absence of out-of-sample validation created potentially significant risks of model overfitting. The results also indicated that the regression analysis provided limited evidence that historical volatility consistently predicts future returns. In summary, it appears that much of the apparent success of the strategy can be attributed to sample-specific characteristics and modeling decisions rather than to a stable relationship between historical volatility and future returns.

A key contribution of this study is the comprehensive review and critical evaluation of the methodology used to develop and assess a quantitative trading strategy under realistic methodological constraints. The findings further emphasize the importance of robust validation procedures and cautious interpretation of historical backtesting results in empirical finance research. More broadly, the results suggest that historical volatility may be more useful as a measure of market uncertainty and prevailing market conditions than as a reliable predictor of future returns…

In short, although the strategy looks very strong in-sample, the authors argue that much of that apparent success is undermined by look-ahead bias, intensive parameter tuning, instability across periods, and no out-of-sample validation.

Another important finding of the paper is that historical volatility is probably more useful as a state variable describing market uncertainty than as a reliable predictor of future returns, and that the spectacular backtest results are more consistent with sample-specific optimization than a robust trading edge.

This is an important contribution. Although the findings are well known to practitioners, the paper draws attention to the severity of system breakdown and will hopefully encourage more formal research on this subject.

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

References

[1] Valls, F. B., & Steurer, E. (2026), When strategies work too well: Volatility-based investing, overfitting and the limits of empirical finance, Journal of Financial Risk Management, 15, 189–211.

Originally Published Here: When Trading Strategies Look Too Good

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Path Dependence in Option P&L

The P&L attribution of an option portfolio is highly important, as it aids in portfolio and risk management. Most often, the daily P&L of an option is broken down into four components: theta, delta, gamma, and vega. This decomposition works well in most cases, especially when the market moves orderly, and the options are at or near the money. However, this breakdown becomes less accurate under more complex market conditions.

Reference [1] extends the usual P&L decomposition framework to longer periods, rather than restricting it to a single day, and also includes P&L originating from higher-order Greeks. The authors perform the analysis both theoretically and empirically, using SPX option data from 2007–2023. They pointed out,

The most important takeaway from Fig. 1 is that, countrary to market wisdom, the differential between implied vol at inception and realized vol is not the main P&L driver for an ATM option. Instead, it is the gamma covariance effect (yellow bar) which takes that crown, over our sixteen years backtest window at least. While (Daviaud & Mukhopadhyay, 2022) studied the volatility premium component extensively, it is much less clear what is behind the gamma covariance effect. That is in spite of this effect having been well known to practioners, on a qualitative level at least, for many years, as illustrated in (Bossu, Strasser, & Guichard, 2005)[Exhibit 2.1.1,p12]. What (3) achieves is that it quantifies this effect precisely, paving the way for its study. In the next section we shed light on it from a mathematical standpoint.

In short, the paper concludes that option P&L is driven not merely by implied minus realized vol, but also by where along the underlying path that realized volatility occurs. The gamma-covariance term is the formal representation of that path dependence.

This paper formalizes an observation that practitioners have made for a long time: the P&L of a delta-hedged option is path dependent. It expresses this path dependence mathematically through a so-called gamma-covariance term.

The paper also demonstrates the importance of P&L attributed to higher-order Greeks such as vanna and volga, providing a more comprehensive framework for understanding option P&L.

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

References

[1] Daviaud, R. & Mukhopadhyay, S. (2023), Option P&L: New Perspectives, J.P. Morgan Quantitative Research, SSRN 4495530.

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Impact of Spot-Volatility Correlation on Option Returns

It is well known that equity indices tend to exhibit a negative correlation with their volatility. There is some research on this relationship, often linking it to the leverage effect. Recently, we observed that the equity market has been behaving unusually, with the spot-volatility correlation turning positive. It is therefore timely to revisit research on this relationship.

Reference [1] studies how changes in the spot-volatility correlation affect expected option returns. The study uses SPX options from 1996 to 2023, realized variance calculated from 5-minute S&P 500 futures data, and a 30-day rolling return-variance correlation. The authors formulate the research problems using the Heston option pricing model under the physical measure. They pointed out,

This paper examines how the leverage effect affects expected index option returns. We rely on the Heston (1993) stochastic volatility framework and the exponential-affine pricing kernel in Heston et al. (2024b) to characterize the relation between the return-variance correlation and expected index call and put returns…

The theoretical analysis predicts a negative (positive) relation between the market leverage effect and call (put) expected returns, and the magnitudes of these relations increase for out-of-the-money contracts for calls. We confirm these theoretical predictions using a long sample of weekly S&P 500 index option returns. The estimated signs are robust across alternative formation days, the exclusion of expiration weeks, longer maturities, and a monthly holding period aligned with the option expiration cycle, and the results remain statistically significant.

In short, the paper concludes that,

• When correlation increases, i.e., becomes less negative, expected call returns decrease and expected put returns increase. Equivalently, a more negative correlation raises expected call returns and lowers expected put returns;
• For calls, the effect becomes substantially stronger OTM; for puts, it becomes weaker OTM.

This article tackles a relatively underexplored but important topic. An interesting irony is that the correlation can become positive when investors aggressively buy out-of-the-money call options as the equity market rallies, thus increasing volatility and causing the correlation to turn positive. However, by doing so, they also reduce the expected returns on their calls.

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

References

[1] Driessen, J., Jeon, J., and Sun, Y. (2026), The Leverage Effect and Expected Option Returns, Working Paper

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