A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study shows big winner stocks significantly impact passive and active investment strategies.
This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contraria…
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
In Part III of this study, we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing prices of the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange. The basic idea is to estimate the strength parameters of the big buyers and the…
We consider PAC-learning a good item from -subsetwise feedback information sampled from a Plackett-Luce probability model, with instance-dependent sample complexity performance. In the setting where subsets of a fixed size can be tested and top-ranked feedback is made available to the learner, we give an algorithm w…
A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial rati…
We consider an ideal closed stock market, in which 100 traders have economic activities. The assets of the traders change through buying and selling stocks. We simulate the assets under conservation of both total currency and total number of stocks. If the traders are identical, then the assets are distributed as a sta…
Framework analyzes stock price co-movement with fundamentals using big data.
The paper proves an equilibrium in a limited stock market participation model with power utilities.
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as columns (features). By e…
This case study tests the possibility of prediction for "success" (or "winner") components of four stock & shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the f…
Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.
Proposes a new method for big portfolio selection using graph-based conditional moments.
Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of …
A new method corrects for bias in selecting the best candidate.
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Mult…
Study the Mexican stock market's interdependency structure from 2000-2019.
New algorithm reduces dynamic regret in non-stationary dueling bandits using a weighted Borda score.
Predict stock trends using news sentiment and technical indicators in Spark.
New method identifies Condorcet winner in dueling bandits with improved sample complexity.
This paper tackles combinatorial pure exploration for dueling bandits, aiming to find the best candidate-position match.
The study aims to explore the strength of causal relationship between stock price search interest and real stock market outcomes on worldwide equity market indices. Such a phenomenon could also be mediated by investor behavior and extent of news coverage. The stock-specific internet search trends data and corresponding…
New method uses geometric properties for better density estimation.
With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework …
Algorithm identifies Copeland winners in dueling bandits with ternary feedback.
We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distribution, which is inspirited by the hierarchical structures prevailing in social organizations. We determine the mean, variance and kurtosis of the Gaussian-Chain distribution to show its heavy-tailed property, and compute the tail distribution tab…
This paper fine-tunes LLMs for stock return prediction using financial news.
A game-theoretic approach to multi-criteria ranking from ordinal data.
The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current conven…
SIREN protocol corrects optimistic winner's scores in LLM evaluation.
ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.
Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challen…
Data-driven decision-making often overestimates benefits due to the winner's curse.
Study finds 'happiness' search data predicts stock returns, suggesting utility needs impact firm performance.
We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response …
Study shows stock price interactions increase during crises due to external stimulus.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Study uses ML to analyze financial behavior in big data.
TimeMCL forecasts diverse time series futures using neural networks and WTA loss.
Forecasting US stock market indices during COVID-19 using machine learning models.
Stochastic LWTA networks resist adversarial attacks while maintaining accuracy.
Paper proposes a method to predict MOBA game winners with calibrated confidence.
aMCL uses annealing to improve hypothesis diversity in ambiguous tasks.
A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.
The relativistic quantum mechanic approach is used to develop a stock market dynamics. The relativistic is conceptional here as the meaning of big external volatility or volatility shock on a financial market. We used a differential geometry approach with the parallel transport of the prices to obtain a direct shift of…
New method optimizes treatment policies to avoid winner's curse.
The increasing availability of "big" (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e. positive or negative opinions) can be used…