Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models

Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-of-the-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors

Identifying Expert Investors on Financial Microblog via Artificial Neural Networks

In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves from the rest of crowd. The Present Work is leveraging 6 years of tweets extracted from the financial platform StockTwits.com, deep diving in its content, and proposing a predictive Neural Network algorithm of Multi-Layer Perceptron type, based on features derived from text, social network and sentiment analysis. Users have been classified based on the performance achieved during the training, consistence of their prediction has been verified throughout the time and, finally, a trading strategy has been proposed based on following the top actors. The outcomes highlighted that expert investors are outperforming the wisdom of the crowd, and the trading schema put together generated a return of 38.6%, in 2015, when S&P500 had a slightly negative balance

주식 공매도 비용 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 조성준.Stock short positions in financial investment can be achieved by borrowing and selling stocks. Such activities involve fees including commissions and stock loan fees. Prediction of such fees is valuable in two ways; historical data enables rigorous back-testing of investment strategies, and predicting the future fees contributes to risk management and execution planning. The fees are highly positively skewed, so that the fees are formed around 0 under normal regime. Such stocks are referred to as ‘general collateral’. On the other hand, those with abnormally high loan fees are said to be ‘special’. As a contribution to the stock short sales fee prediction, the thesis focuses on predicting such specialness via data mining and machine learning techniques. As a result, the models are proposed to predict the specialness, and performance baselines are produced by comparing well-established machine learning techniques.주식 공매도를 위한 주식 대여는 비용이 발생한다. 해당 비용을 예측하는 것은 두 가지 측면에서 투자자에게 유리하다. 먼저, 과거 공매도 비용 데이터가 정확하다면 투자 전략 백테스팅의 정확도 향상을 기대할 수 있다. 또한, 미래의 공매도 데이터를 예측한다면 투자 위험 관리와 전략 실행 최적화의 재료가 된다. 주식 대여 비용의 분포는 아주 치우쳐져 있다. (양의 왜도) 일반적으로 0에 가까운 값을 가지는데 이를 문헌에선 일반 담보 (General Collateral)의 상태에 있다고 한다. 하지만 공매도 수요가 높은 상황에서는 공매도 비용이 크게 증가하는 것을 관찰할 수 있는데 이를 특이한 (Special) 상태에 있다고 한다. 본 연구는 주식 공매도 비용 예측에 공헌하고자 특이 주식과 일반 담보 주식을 분류하는 모델을 개발한다. 특히, 머신러닝 및 데이터 마이닝 방법을 사용한다. 분류 모델을 제안하는 것과 더불어 다양한 기법을 적용함으로써 해당 문제의 베이스라인을 제공한다.Chapter 1. Introduction 1 Chapter 2. Literature Review 6 Chapter 3. Proposed Framework 12 Chapter 4. Models 22 Chapter 5. Experimental Results 27 Chapter 6. Conclusion 39 Bibliography 41 Abstract in Korean 46석

Reinforcement learning for portfolio optmization

In this study, the potential of using Reinforcement Learning for Portfolio Optimization is investigated, considering the constraints set by the stock market, such as liquidity, slippage, and transaction costs. Five Deep Reinforcement Learning (DRL) agents are trained in two different environments to test the agents' ability to learn the best trading strategies to allocate assets, expecting to generate higher cumulative returns. All agents used are model-free and already optimized for financial problems, using the FinRL library. Therefore, the state-space has a high dimension, as found in the financial market environments. The two proposed environments use market data from US stocks, and one of them also uses Finsent data, an alternative data source that contains the news sentiment for all the stocks that are part of Dow Jones Industrial Average (DJIA). A series of backtesting experiments were performed from the beginning of 2019 to the beginning of 2020 and compared the two environments and how the agents performed against the DJIA. All the results were assessed with the pyfolio Python library, which uses all standard metrics to evaluate portfolio performance. Some algorithms increased the cumulative returns compared to the first dataset. The best result obtained outperformed DJIA by a significant amount and a smaller drawdown

Forecasting changes in the South African volatility index. A comparison of methods

Increased financial regulation with tougher capital standards and additional capital buffers has made understanding volatility in financial markets more imperative. This study investigates various forecasting techniques in their ability to forecast the South African Volatility Index (SAVI). In particular, a time-delay neural network’s forecasting ability is compared to more traditional methods. A comparison of the residual errors of all the forecasting tools used suggests that the time-delay neural network and the historical average model have superior forecasting ability over traditional forecasting models. From a practical perspective, this suggests that the historical average model is the best forecasting tool used in this study, as it is less computationally expensive to implement compared to the neural network.  Furthermore, the results suggest that the SAVI is extremely difficult to forecast, with the volatility index being purely a gauge of investor sentiment in the market, rather than being seen as a potential investment opportunity.&nbsp

Technical analysis in the foreign exchange market

This article introduces the subject of technical analysis in the foreign exchange market, with emphasis on its importance for questions of market efficiency. Technicians view their craft, the study of price patterns, as exploiting traders’ psychological regularities. The literature on technical analysis has established that simple technical trading rules on dollar exchange rates provided 15 years of positive, risk-adjusted returns during the 1970s and 80s before those returns were extinguished. More recently, more complex and less studied rules have produced more modest returns for a similar length of time. Conventional explanations that rely on risk adjustment and/or central bank intervention are not plausible justifications for the observed excess returns from following simple technical trading rules. Psychological biases, however, could contribute to the profitability of these rules. We view the observed pattern of excess returns to technical trading rules as being consistent with an adaptive markets view of the world.Foreign exchange rates

Option valuation under no-arbitrage constraints with neural networks

In this paper, we start from the no-arbitrage constraints in option pricing and develop a novel hybrid gated neural network (hGNN) based option valuation model. We adopt a multiplicative structure of hidden layers to ensure model differentiability. We also select the slope and weights of input layers to satisfy the no-arbitrage constraints. Meanwhile, a separate neural network is constructed for predicting option-implied volatilities. Using S&P 500 options, our empirical analyses show that the hGNN model substantially outperforms well-established alternative mod els in the out-of-sample forecasting and hedging exercises. The superior prediction performance stems from our model’s ability in describing options on the boundary, and in offering analytical expressions for option Greeks which generate better hedging results