Integrating feature selection and regression methods with technical indicators for predicting Apple Inc. stock prices ArXiv ID: 2310.09903 “View on arXiv” Authors: Unknown Abstract Stock price prediction is influenced by a variety of factors, including technical indicators, which makes Feature selection crucial for identifying the most relevant predictors. This study examines the impact of feature selection on stock price prediction accuracy using technical indicators. A total of 123 technical indicators and 10 regression models were evaluated using 13 years of Apple Inc. data. The primary goal is to identify the best combination of indicators and models for improved forecasting. The results show that a 3-day time window provides the highest prediction accuracy. Model performance was assessed using five error-based metrics. Among the models, Linear Regression and Ridge Regression achieved the best overall performance, each with a Mean Squared Error (MSE) of 0.00025. Applying feature selection significantly improved model accuracy. For example, the Multi-layered Perceptron Regression using Forward Selection improved by 56.47% over its baseline version. Support Vector Regression improved by 67.42%, and Linear Regression showed a 76.7% improvement when combined with Forward Selection. Ridge Regression also demonstrated a 72.82% enhancement. Additionally, Decision Tree, K-Nearest Neighbor, and Random Forest models showed varying levels of improvement when used with Backward Selection. The most effective technical indicators for stock price prediction were found to be Squeeze_pro, Percentage Price Oscillator, Thermo, Decay, Archer On-Balance Volume, Bollinger Bands, Squeeze, and Ichimoku. Overall, the study highlights that combining selected technical indicators with appropriate regression models can significantly enhance the accuracy and efficiency of stock price predictions. ...

Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model ArXiv ID: 2310.09622 “View on arXiv” Authors: Unknown Abstract Cryptocurrencies and Bitcoin, in particular, are prone to wild swings resulting in frequent jumps in prices, making them historically popular for traders to speculate. A better understanding of these fluctuations can greatly benefit crypto investors by allowing them to make informed decisions. It is claimed in recent literature that Bitcoin price is influenced by sentiment about the Bitcoin system. Transaction, as well as the popularity, have shown positive evidence as potential drivers of Bitcoin price. This study considers a bivariate jump-diffusion model to describe Bitcoin price dynamics and the number of Google searches affecting the price, representing a sentiment indicator. We obtain a closed formula for the Bitcoin price and derive the Black-Scholes equation for Bitcoin options. We first solve the corresponding Bitcoin option partial differential equation for the pricing process by introducing artificial neural networks and incorporating multi-layer perceptron techniques. The prediction performance and the model validation using various high-volatile stocks were assessed. ...

Prime Match: A Privacy-Preserving Inventory Matching System ArXiv ID: 2310.09621 “View on arXiv” Authors: Unknown Abstract Inventory matching is a standard mechanism/auction for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of finding such matches between their clients. The related stocks can be traded without adversely impacting the market price for either client. If matches between clients are found, the bank can offer the trade at advantageous rates. If no match is found, the parties have to buy or sell the stock in the public market, which introduces additional costs. A problem with the process as it is presently conducted is that the involved parties must share their order to buy or sell a particular stock, along with the intended quantity (number of shares), to the bank. Clients worry that if this information were to leak somehow, then other market participants would become aware of their intentions and thus cause the price to move adversely against them before their transaction finalizes. We provide a solution, Prime Match, that enables clients to match their orders efficiently with reduced market impact while maintaining privacy. In the case where there are no matches, no information is revealed. Our main cryptographic innovation is a two-round secure linear comparison protocol for computing the minimum between two quantities without preprocessing and with malicious security, which can be of independent interest. We report benchmarks of our Prime Match system, which runs in production and is adopted by J.P. Morgan. The system is designed utilizing a star topology network, which provides clients with a centralized node (the bank) as an alternative to the idealized assumption of point-to-point connections, which would be impractical and undesired for the clients to implement in reality. Prime Match is the first secure multiparty computation solution running live in the traditional financial world. ...

Sparse Index Tracking via Topological Learning ArXiv ID: 2310.09578 “View on arXiv” Authors: Unknown Abstract In this research, we introduce a novel methodology for the index tracking problem with sparse portfolios by leveraging topological data analysis (TDA). Utilizing persistence homology to measure the riskiness of assets, we introduce a topological method for data-driven learning of the parameters for regularization terms. Specifically, the Vietoris-Rips filtration method is utilized to capture the intricate topological features of asset movements, providing a robust framework for portfolio tracking. Our approach has the advantage of accommodating both $\ell_1$ and $\ell_2$ penalty terms without the requirement for expensive estimation procedures. We empirically validate the performance of our methodology against state-of-the-art sparse index tracking techniques, such as Elastic-Net and SLOPE, using a dataset that covers 23 years of S&P500 index and its constituent data. Our out-of-sample results show that this computationally efficient technique surpasses conventional methods across risk metrics, risk-adjusted performance, and trading expenses in varied market conditions. Furthermore, in turbulent markets, it not only maintains but also enhances tracking performance. ...

A generalization of the rational rough Heston approximation ArXiv ID: 2310.09181 “View on arXiv” Authors: Unknown Abstract Previously, in [“GR19”], we derived a rational approximation of the solution of the rough Heston fractional ODE in the special case λ= 0, which corresponds to a pure power-law kernel. In this paper we extend this solution to the general case of the Mittag-Leffler kernel with λ\geq 0. We provide numerical evidence of the convergence of the solution. ...

Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis ArXiv ID: 2311.06273 “View on arXiv” Authors: Unknown Abstract The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across different areas, our study investigates ChatGPT’s capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data on platforms like Twitter to offer insightful predictions about stock trends. We focus on determining if a tweet has a positive, negative, or neutral effect on two big tech giants Microsoft and Google’s stock value. Our findings highlight a positive link between ChatGPT’s evaluations and the following days stock results for both tech companies. This research enriches our view on ChatGPT’s adaptability and emphasizes the growing importance of AI in shaping financial market forecasts. ...

Uncovering Market Disorder and Liquidity Trends Detection ArXiv ID: 2310.09273 “View on arXiv” Authors: Unknown Abstract The primary objective of this paper is to conceive and develop a new methodology to detect notable changes in liquidity within an order-driven market. We study a market liquidity model which allows us to dynamically quantify the level of liquidity of a traded asset using its limit order book data. The proposed metric holds potential for enhancing the aggressiveness of optimal execution algorithms, minimizing market impact and transaction costs, and serving as a reliable indicator of market liquidity for market makers. As part of our approach, we employ Marked Hawkes processes to model trades-through which constitute our liquidity proxy. Subsequently, our focus lies in accurately identifying the moment when a significant increase or decrease in its intensity takes place. We consider the minimax quickest detection problem of unobservable changes in the intensity of a doubly-stochastic Poisson process. The goal is to develop a stopping rule that minimizes the robust Lorden criterion, measured in terms of the number of events until detection, for both worst-case delay and false alarm constraint. We prove our procedure’s optimality in the case of a Cox process with simultaneous jumps, while considering a finite time horizon. Finally, this novel approach is empirically validated by means of real market data analyses. ...

Statistical arbitrage portfolio construction based on preference relations ArXiv ID: 2310.08284 “View on arXiv” Authors: Unknown Abstract Statistical arbitrage methods identify mispricings in securities with the goal of building portfolios which are weakly correlated with the market. In pairs trading, an arbitrage opportunity is identified by observing relative price movements between a pair of two securities. By simultaneously observing multiple pairs, one can exploit different arbitrage opportunities and increase the performance of such methods. However, the use of a large number of pairs is difficult due to the increased probability of contradictory trade signals among different pairs. In this paper, we propose a novel portfolio construction method based on preference relation graphs, which can reconcile contradictory pairs trading signals across multiple security pairs. The proposed approach enables joint exploitation of arbitrage opportunities among a large number of securities. Experimental results using three decades of historical returns of roughly 500 stocks from the S&P 500 index show that the portfolios based on preference relations exhibit robust returns even with high transaction costs, and that their performance improves with the number of securities considered. ...

Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions ArXiv ID: 2310.07427 “View on arXiv” Authors: Unknown Abstract We propose a time series forecasting method named Quantum Gramian Angular Field (QGAF). This approach merges the advantages of quantum computing technology with deep learning, aiming to enhance the precision of time series classification and forecasting. We successfully transformed stock return time series data into two-dimensional images suitable for Convolutional Neural Network (CNN) training by designing specific quantum circuits. Distinct from the classical Gramian Angular Field (GAF) approach, QGAF’s uniqueness lies in eliminating the need for data normalization and inverse cosine calculations, simplifying the transformation process from time series data to two-dimensional images. To validate the effectiveness of this method, we conducted experiments on datasets from three major stock markets: the China A-share market, the Hong Kong stock market, and the US stock market. Experimental results revealed that compared to the classical GAF method, the QGAF approach significantly improved time series prediction accuracy, reducing prediction errors by an average of 25% for Mean Absolute Error (MAE) and 48% for Mean Squared Error (MSE). This research confirms the potential and promising prospects of integrating quantum computing with deep learning techniques in financial time series forecasting. ...

The Specter (and Spectra) of Miner Extractable Value ArXiv ID: 2310.07865 “View on arXiv” Authors: Unknown Abstract Miner extractable value (MEV) refers to any excess value that a transaction validator can realize by manipulating the ordering of transactions. In this work, we introduce a simple theoretical definition of the ‘cost of MEV’, prove some basic properties, and show that the definition is useful via a number of examples. In a variety of settings, this definition is related to the ‘smoothness’ of a function over the symmetric group. From this definition and some basic observations, we recover a number of results from the literature. ...