Look-ahead Bias Research Articles

Lookahead Bias in Pretrained Language Models

Lookahead Bias in Pretrained Language Models

Assessing Look-Ahead Bias in Stock Return Predictions Generated by GPT Sentiment Analysis

Assessing Look-Ahead Bias in Stock Return Predictions Generated by GPT Sentiment Analysis

Robust monitoring machine: a machine learning solution for out-of-sample R^2-hacking in return predictability monitoring

The out-of-sample R^2 is designed to measure forecasting performance without look-ahead bias. However, researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample. Using ensemble machine learning techniques, we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations. We apply this approach to robust monitoring, exploiting a dynamic shrinkage effect by switching between a proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces the variance of its relative performance by 46% while avoiding the out-of-sample R^2-hacking problem. Our approach, as a final touch, can further enhance the performance and stability of forecasts from any models and methods.

Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM.

Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. This study proposes an ensemble support vector machine for improving the accuracy of stock price movement predictions. Then, it predicts the future movement of SPDR S&P 500 Index Exchange Traded Funds using the rolling window approach to prevent look-ahead bias. Through comparing various techniques for generating sentiment, our results show that using the FinBERT model for sentiment analysis yields the best results, with an F1-score that is 4-5% higher than other techniques. Additionally, the proposed ensemble support vector machine improves the accuracy of stock price movement predictions when compared to the original support vector machine in a series of experiments.

Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis

Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis

Unintended look-ahead bias in out-of-sample forecasting

ABSTRACT This article shows that out-of-sample tests are susceptible to look-ahead bias not only to multiple testing problem that is emphasized in the literature. A forecaster often constructs a well-performing model without trial and error but with an intuition that is derived from observed empirical patterns in the test sample. Such an intuition, however, is unavailable in the beginning of the test sample. Therefore, the reported forecasting performance in an out-of-sample test is possibly exaggerated, although a forecaster simply utilizes her expertise without any intended p-hacking or fishing. A stylized forecasting model with an example of stock market return predictability quantitatively demonstrates this unintended look-ahead bias in out-of-sample tests.

Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market

This paper proposes a long short-term memory (LSTM) neural network model to predict daily stock price movements based on asset pricing factors (i.e., the five factors proposed by Fama and French, and the short-term momentum factor). Based on three independent experiments, we systematically evaluate the explanatory power and the predictive power of the LSTM model by employing 3316 A-share listed companies in the Shanghai and Shenzhen stock exchanges from the in-sample period January 1, 2008 to December 31, 2019. Furthermore, we propose a four-step approach to dynamically update the underlying stocks in different portfolios based on the empirical findings. All portfolios are simulated using out-of-sample data (i.e., from January 1, 2020, to May 31, 2021) to avoid look-ahead bias. The trading results suggest that our dynamic investment strategies are superior to the benchmark index and are able to generate significant returns with relatively low risks.

Testing Factor Models in the Cross-Section

The standard full-sample time-series asset pricing test suffers from poor statistical properties, look-ahead bias, constant-beta assumptions, and rejects models when average factor returns deviate from risk premia. We therefore confront prominent equity pricing models with the classical Fama and MacBeth (1973) cross-sectional test. For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) cross-sectional factor risk premium estimates are generally far below the average factor excess returns; and (iii) they are usually not statistically significant. Overall, all new factor models are inconsistent with no-arbitrage pricing and cannot accurately explain the cross-section of stock returns.

Future Stock Market Prediction Using Machine Learning

The Attributes of the stock or security exchange continuously changing over year. While only one thing which cannot be change in the stock market i.e. volatibility, It is the most powerful technique because can make money while sleeping by investing money in the stock market. In our project we make a website, That is predict performance of stock asset in the future and help the user in which company they should invest so they can make maximum profit. For this predictions we various types of machine learning algorithms, But in recent studies suggested that support vector machine(SVM) is most commonly used for make precisions, Which is lead to the look-ahead bias, Which was later tackeled by the coupling it with the window approach to improve the accuracy of the algorithm. But on other hand we use the linear regression algorithm to making the predictions. The results which is produced by the website shows that our method is more accurate than the support vector machine(SVM). Furthermore, If any new user visit the website which does not have any knowledge about stock investment for such a users we can links to the various articles and videos which is brush up his knowledge.

A machine learning, bias-free approach for predicting business success using Crunchbase data

Predicting the success of a business venture has always been a struggle for both practitioners and researchers. However, thanks to companies that aggregate data about other firms, it has become possible to create and validate predictive models based on an unprecedented amount of real-world examples. In this study, we use data obtained from one of the largest platforms integrating business information – Crunchbase. Our final training set consisted of 213 171 companies.This work aims to create a predictive model based on machine learning for the purpose of forecasting a company’s success. Many similar attempts have been made in recent years. Plenty of those experiments, often conducted with the use of data gathered from several different sources, reported promising results. However, we found that very often they were significantly biased by their use of data containing information that was a direct consequence of a company reaching some level of success (or failure). Such an approach is a classic example of the look-ahead bias. It leads to very optimistic test results, but any attempt at using such an approach in a real-world scenario may result in dramatic consequences. We designed our experiments in a way that would prevent the leaking of any information unavailable at the decision moment to the training set.We compared three algorithms – logistic regression, support vector machine, and the gradient boosting classifier. Despite the conscious decision to limit the number of predictors, we reached very promising results in terms of precision, recall, and F1 scores which, for the best model, were 57%, 34%, and 43% respectively. The best outcomes were obtained with the gradient boosting classifier. We give detailed information about the importance of different features, with the top three being country and region that the company operates in and the company’s industry. Our model can be applied directly as a decision support system for different types of venture capital funds.

Economic Narratives and Market Outcomes: A Semi-supervised Topic Modeling Approach

I employ the seeded Latent Dirichlet Process (sLDA) model in natural language processing to extract the narratives discussed by Shiller (2019) from nearly seven million New York Times articles over the past 150 years. The estimation scheme is designed to avoid any look-ahead bias in constructing the monthly narrative weights. Among the narratives considered, the most important one is Panic, which encompasses various stress- and anxiety-related themes including economic downturns, wars, political tensions, and epidemics. I find that Panic and a narrative index that loads heavily on Panic are strong positive predictors of excess U.S. market return and negative predictors of both realized and implied market volatility. I document empirical support for Panic as a proxy for time-varying risk aversion, consistent with a univariate version of the intertemporal capital asset pricing model (ICAPM). The predictability of narratives over market returns holds at both market and portfolio level and at both monthly and daily interval, and importantly is increasing over time.

Unintended Look-Ahead Bias in Out-of-Sample Forecasting

Unintended Look-Ahead Bias in Out-of-Sample Forecasting

A Labeling Method for Financial Time Series Prediction Based on Trends.

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

The Economic Value of Equity Implied Volatility Forecasting with Machine Learning

We evaluate the importance of nonlinear interactions in volatility forecasting by comparing the predictive power of decision tree ensemble models relative to classical ones for normalized at-the-money implied volatility innovations. We measure the economic significance of these predictions in cross-sectional and time series pricing tests of delta-hedged option returns. Classification tree ensembles outperform a multinomial logit classifier by 0.35% to 0.46% monthly abnormal returns in delta-hedged option portfolio sorts on volatility innovation forecast data, while regression tree ensembles outperform OLS and LASSO models by 0.03% to 0.14%. Since the predictive variables are the same across all models, these performance differences likely capture the value of nonlinear interactions in implied volatility forecasts. Our results are robust to look-ahead bias and model over-fitting.

Portfolio Management of High Growth Firms and Technical Buy Points

This paper shows that current earnings and prices at or near new highs can be combined with the Average Directional Index (DM) technical indicator to generate excess return in the market. Excess returns are statistically significant via difference in means tests (robust to recession only periods, and additional timeframes) and CAPM, Fama and French 3 and 5 factor models. The model is robust to alternate weight specification and has consistent results across multiple start dates. The system is replicable and has a win rate of 63% turning $100,000 into $33,000,000. It is constructed without lookahead bias.

A Firm-Level Analysis of the Cross-Sectional Relation between Expected Returns and Expected Idiosyncratic Volatility in the Korean Stock Market

Using monthly firm-level data from the Korean stock market from January 1992 to June 2016, we examine the cross-sectional relation between expected returns and expected idiosyncratic volatility. Considering the time varying property of idiosyncratic volatility, we use EGARCH model to estimate the conditional out-of-sample expected idiosyncratic volatility to avoid the problem of look-ahead-bias. Our main results are as follows. Our equal-weighted portfolio analysis that exclude any control variables exhibits that as conditional out-of-sample expected idiosyncratic volatility increase, expected returns tend to decrease. According to the equal-weighted Fama-MacBeth cross-sectional regression that includes systematic beta, size, book-to-market ratio factor, momentum, liquidity, return reversal and asset growth on the firm level, conditional out-of-sample expected idiosyncratic volatility consistently have a significantly negative relation with expected returns. This relation is also observed in the periods after the currency crisis and the global financial crisis, in the non-January sample, and in both up-phases and down-phases. Interestingly, we observe a spurious positive relation induced by look-ahead bias between contemporaneous conditional in-sample expected idiosyncratic volatility and expected returns. Our empirical findings suggest that the significantly negative relation between conditional out-of-sample expected idiosyncratic volatility and expected returns observed in the Korean stock market may be an idiosyncratic volatility anomaly.

Does Expected Idiosyncratic Volatility Explain the Cross-Section of Expected Returns?

Expected idiosyncratic volatility and its positive relation to expected returns of Fu (2009) can be closely replicated, but only when we include information up to time t to estimate the idiosyncratic volatility at time t. Since this involves look-ahead bias, we re-estimate expected idiosyncratic volatility using information only up to time t - 1. We find no significant relation between idiosyncratic volatility and returns, and our results are robust to the sample periods extended to before and after that of Fu (2009). Our findings are consistent with the fact that idiosyncratic risk is not priced.

Volatility-Managed Portfolio: Does It Really Work?

In this article, the authors find that a typical application of volatility-timing strategies to the stock market suffers from look-ahead bias, despite existing evidence on the success of the strategies at the stock level. After correcting this bias, the strategy becomes very difficult to implement in practice because its maximum drawdown is 68%–93% in almost all cases. Moreover, the strategy outperforms the market only during the financial crisis period. The authors also consider three alternative volatility-timing strategies and find that they do not outperform the market either. Their results show that one cannot easily beat the market via timing the market alone. TOPICS:Portfolio construction, statistical methods, risk management Key Findings • A typical application of volatility-timing strategies to the aggregate stock market, instead of at the stock level, can be more susceptible to look-ahead bias. • After correcting the bias, the strategy is difficult to implement due to large drawdowns, and it performs poorly over time except during the recent financial crisis period when volatility was extremely high. • One cannot easily beat the market via volatility-timing the market alone.

An Efficient Approach for Removing Look-Ahead Bias in the Least Square Monte Carlo Algorithm: Leave-One-Out

The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz (2001) is widely used for pricing American options. The LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm for efficiently eliminating look-ahead bias. We also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues. The LOOLSM method can be extended to other regression-based algorithms improving the LSM method.

A Practitioner's Defense of Return Predictability

Revisiting the issue of return predictability, we show there is substantial predictive power in combining forecasting variables. We apply correlation screening to combine twenty variables that have been proposed in the return predictability literature, and demonstrate forecasting power at a six-month horizon. We illustrate the economic significance of return predictability through a walk-forward simulation, which takes positions in SPY proportional to the model forecast equity risk premium. The simulated strategy yields annual returns more than twice that of the buy-and-hold strategy, with a Sharpe ratio four times as large. To eliminate look-ahead bias, we perform additional simulations including variables only as they are discovered in the literature. Results show similar annual returns and Sharpe ratios. While a market-timing strategy outperforms the market, it is difficult to implement.