Equities

Value-at-Risk-Based Portfolio Insurance: Performance Evaluation and Benchmarking Against CPPI in a Markov-Modulated Regime-Switching Market ArXiv ID: 2305.12539 “View on arXiv” Authors: Unknown Abstract Designing dynamic portfolio insurance strategies under market conditions switching between two or more regimes is a challenging task in financial economics. Recently, a promising approach employing the value-at-risk (VaR) measure to assign weights to risky and riskless assets has been proposed in [“Jiang C., Ma Y. and An Y. “The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis” , International Review of Financial Analysis 18(4) (2009): 185-197”]. In their study, the risky asset follows a geometric Brownian motion with constant drift and diffusion coefficients. In this paper, we first extend their idea to a regime-switching framework in which the expected return of the risky asset and its volatility depend on an unobservable Markovian term which describes the cyclical nature of asset returns in modern financial markets. We then analyze and compare the resulting VaR-based portfolio insurance (VBPI) strategy with the well-known constant proportion portfolio insurance (CPPI) strategy. In this respect, we employ a variety of performance evaluation criteria such as Sharpe, Omega and Kappa ratios to compare the two methods. Our results indicate that the CPPI strategy has a better risk-return tradeoff in most of the scenarios analyzed and maintains a relatively stable return profile for the resulting portfolio at the maturity. ...

Non-parametric cumulants approach for outlier detection of multivariate financial data ArXiv ID: 2305.10911 “View on arXiv” Authors: Unknown Abstract In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a concave minimization problem. Then, we show that the CGF maximization approach can be interpreted as an extension of the standard principal component technique. Therefore, for validation and testing, we provide a thorough comparison of our methodology with two other projection-based approaches both on artificial and real-world financial data. Finally, we apply our method as an early detector for financial crises. ...

Support for Stock Trend Prediction Using Transformers and Sentiment Analysis ArXiv ID: 2305.14368 “View on arXiv” Authors: Unknown Abstract Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of stocks have been based on Recurrent Neural Networks (RNNs). However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows. This paper also introduces a novel dataset containing daily technical stock data and top news headline data spanning almost three years. Stock prediction based solely on technical data can suffer from lag caused by the inability of stock indicators to effectively factor in breaking market news. The use of sentiment analysis on top headlines can help account for unforeseen shifts in market conditions caused by news coverage. We measure the performance of our model against RNNs over sequence lengths spanning 5 business days to 30 business days to mimic different length trading strategies. This reveals an improvement in directional accuracy over RNNs as sequence length is increased, with the largest improvement being close to 18.63% at 30 business days. ...

Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms ArXiv ID: 2309.00618 “View on arXiv” Authors: Unknown Abstract Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. We report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer (up to 10 seconds). Results from this study may improve by further tuning the individual parameters or introducing more exogenous variables. ...

NYSE Price Correlations Are Abitrageable Over Hours and Predictable Over Years ArXiv ID: 2305.08241 “View on arXiv” Authors: Unknown Abstract Trade prices of about 1000 New York Stock Exchange-listed stocks are studied at one-minute time resolution over the continuous five year period 2018–2022. For each stock, in dollar-volume-weighted transaction time, the discrepancy from a Brownian-motion martingale is measured on timescales of minutes to several days. The result is well fit by a power-law shot-noise (or Gaussian) process with Hurst exponent 0.465, that is, slightly mean-reverting. As a check, we execute an arbitrage strategy on simulated Hurst-exponent data, and a comparable strategy in backtesting on the actual data, obtaining similar results (annualized returns $\sim 60$% if zero transaction costs). Next examining the cross-correlation structure of the $\sim 1000$ stocks, we find that, counterintuitively, correlations increase with time lag in the range studied. We show that this behavior that can be quantitatively explained if the mean-reverting Hurst component of each stock is uncorrelated, i.e., does not share that stock’s overall correlation with other stocks. Overall, we find that $\approx 45$% of a stock’s 1-hour returns variance is explained by its particular correlations to other stocks, but that most of this is simply explained by the movement of all stocks together. Unexpectedly, the fraction of variance explained is greatest when price volatility is high, for example during COVID-19 year 2020. An arbitrage strategy with cross-correlations does significantly better than without (annualized returns $\sim 100$% if zero transaction costs). Measured correlations from any single year in 2018–2022 are about equally good in predicting all the other years, indicating that an overall correlation structure is persistent over the whole period. ...

Towards Generalizable Reinforcement Learning for Trade Execution ArXiv ID: 2307.11685 “View on arXiv” Authors: Unknown Abstract Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance. ...

Behavioral CorporateFinance ArXiv ID: ssrn-288257 “View on arXiv” Authors: Unknown Abstract Managers and corporate directors need to recognize two key behavioral impediments that obstruct the process of value maximization, one internal to the firm and Keywords: Value Maximization, Behavioral Impediments, Corporate Governance, Management Decision Making, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is conceptual and descriptive, focusing on psychological biases and behavioral theory with minimal mathematical formalism or quantitative models; empirical evidence is cited anecdotally or through references without presenting backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal:<br>Identify behavioral impediments to value maximization"] --> B["Data Inputs:<br>Corporate governance structures & management decisions"] B --> C["Methodology:<br>Analysis of equities & behavioral finance theories"] C --> D{"Computational Process:<br>Assess impact of internal vs. external impediments"} D --> E["Key Findings:<br>Managers must overcome internal biases &<br>external governance misalignments for value maximization"]

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3893357 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced statistical analysis on a large, unique dataset of institutional trades, focusing on empirical performance metrics and counterfactuals. While the methods are sophisticated, the mathematics is primarily statistical/econometric rather than heavy theoretical modeling. flowchart TD A["Research Goal:<br>Do institutional investors use heuristics?<br>Are they consistent in buying vs selling?"] --> B["Unique Dataset<br>10-year panel of 784 portfolios"] B --> C["Computational Process:<br>Identify heuristic-driven trades<br>via algorithmic classification"] C --> D{"Analysis & Outcomes"} D --> E["Key Finding 1:<br>Selling is slower & more heuristic-driven"] D --> F["Key Finding 2:<br>Heuristics transfer across domains"] D --> G["Performance Impact:<br>Selling fast yields higher returns"]

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2021 Edition ArXiv ID: ssrn-3825823 “View on arXiv” Authors: Unknown Abstract The equity risk premium is the price of risk in equity markets, and it is not just a key input in estimating costs of equity and capital in both corporate finan Keywords: equity risk premium, cost of equity, capital asset pricing model, valuation, risk pricing, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses foundational finance equations (CAPM, multi-factor models) with minimal advanced derivation, placing math complexity low. However, it heavily relies on historical data, surveys, and real-world market data (default spreads, option prices) to estimate and compare equity risk premiums, making it highly empirical and implementation-focused. flowchart TD A["Research Goal: Determine ERP<br>for Corporate Valuation"] --> B["Key Methodology: Historical Analysis"] B --> C["Data Inputs: Historical<br>Stock Returns vs<br>Risk-Free Rates"] C --> D["Computational Process:<br>Calculate Average Historical ERP<br>& Adjust for Market Conditions"] D --> E["Key Findings: ERP is unstable<br>Context-dependent; Required for<br>accurate Cost of Equity &<br>Valuation models"]

How Competitive is the Stock Market? Theory, Evidence from Portfolios, and Implications for the Rise of Passive Investing ArXiv ID: ssrn-3821263 “View on arXiv” Authors: Unknown Abstract The conventional wisdom in finance is that competition is fierce among investors: if a group changes its behavior, others adjust their strategies such that noth Keywords: Market Efficiency, Investor Behavior, Game Theory, Strategic Interaction, Equities Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs a semi-structural economic model with equilibrium conditions, endogenous elasticities, and formal estimation challenges (reflection problem, endogeneity), requiring advanced mathematics. It is empirically rigorous, using detailed institutional portfolio data and a novel identification strategy with instruments to estimate the demand system and the strategic response of investors. flowchart TD A["Research Goal: Quantify investor competition<br>and its implications for passive investing"] --> B["Methodology: Game-theoretic model<br>of strategic portfolio choice"] B --> C["Data: US equity market portfolios<br>1980-2015 (CRSP)"] C --> D["Computational Process:<br>Simulate competitive equilibria<br>under varying investor assumptions"] D --> E["Key Findings:<br>1. Competition is strong but incomplete<br>2. Passive investing reduces competition<br>3. Market efficiency varies with investor structure"]