Certain Investigations of prediction on Stock trend using various Optimization Techniques

A stock price represents a company’s value at any given point, trends of the same will be very volatile because of different trading activities, supply and demand of stocks, and companies’ financial outcomes. Predicting the correlation between price, time, and various other variables in any stock trend is an essential need for portfolio optimization. The model of LSTM(Long Short Term Memory) recurrent neural networks (RNN) is the optimal prediction method, with LSTM used for understanding temporal dependencies, which is well known for processing and understanding continuous data points, The above model gives structural integrity to most of the time-series data analysis. The stock market produces a vast amount of data, there will be fluctuation of prices every second, so training Neural Networks for an enormous amount of data takes extensive time, We are performing certain investigations on boosting the accuracy and reducing the time taken to train by further enhancing the above-given model, with modified versions of Adam, RMSProp, and AdaGrad optimization methods

Stock Prediction using GAN and Sentiment Analysis

Tweet sentiment analysis can be used to forecast stock market movements. Examining the attitude expressed in tweets about a company or industry might reveal more about how the public perceives and thinks about it. This information can then be utilised to forecast potential changes in the stock price of the firm or industry. This paper proposes a method for forecasting the stock market based on sentiment analysis of Twitter-sourced messages. To create the dataset, information about various companies via the Twitter API is mapped and linked with their stock prices. This paper employs a Generative Adversarial Networks-based framework that integrates three GAN models with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). To evaluate and compare the efficacy of the proposed model, the Root Mean Square Error (RMSE) is employed as a metric. The stock price and sentiment score of each company were individually assessed to determine which of the three GAN models generated the most accurate outcomes

Extracting fine-grained economic events from business news

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50{%} are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap

A multi-channel cross-residual deep learning framework for news-oriented stock movement prediction

Stock market movement prediction remains challenging due to random walk characteristics. Yet through a potent blend of input parameters, a prediction model can learn sequential features more intelligently. In this paper, a multi-channel news-oriented prediction system is developed to capture intricate moving patterns of the stock market index. Specifically, the system adopts the temporal causal convolution to process historical index values due to its capability in learning long-term dependencies. Concurrently, it employs the Transformer Encoder for qualitative information extraction from financial news headlines and corresponding preview texts. A notable configuration to our multi-channel system is an integration of cross-residual learning between different channels, thereby allowing an earlier and closer information fusion. The proposed architecture is validated to be more efficient in trend forecasting compared to independent learning, by which channels are trained separately. Furthermore, we also demonstrate the effectiveness of involving news content previews, improving the prediction accuracy by as much as 3.39%

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201

On the Economic Significance of Stock Market Prediction and the No Free Lunch Theorem

Forecasting of stock market returns is a challenging research activity that is now expanding with the availability of new data sources, markets, financial instruments, and algorithms. At its core, the predictability of prices still raises important questions. Here we discuss the economic significance of the prediction accuracy. To develop this question, we collect the daily series prices of almost half of the publicly traded companies around the world over a period of ten years and formulate some trading strategies based on their prediction. Proper visualization of these data together with the use of the No Free Lunch theoretical framework give some unexpected results that show how the a priori less accurate algorithms and inefficient strategies can offer better results than the a priori best alternatives in some particular subsets of data that have a clear interpretation in terms of economic sectors and regions.2018-201

The irruption of cryptocurrencies into Twitter cashtags: a classifying solution

There is a consensus about the good sensing characteristics of Twitter to mine and uncover knowledge in financial markets, being considered a relevant feeder for taking decisions about buying or holding stock shares and even for detecting stock manipulation. Although Twitter hashtags allow to aggregate topic-related content, a specific mechanism for financial information also exists: Cashtag. However, the irruption of cryptocurrencies has resulted in a significant degradation on the cashtag-based aggregation of posts. Unfortunately, Twitter' users may use homonym tickers to refer to cryptocurrencies and to companies in stock markets, which means that filtering by cashtag may result on both posts referring to stock companies and cryptocurrencies. This research proposes automated classifiers to distinguish conflicting cashtags and, so, their container tweets by analyzing the distinctive features of tweets referring to stock companies and cryptocurrencies. As experiment, this paper analyses the interference between cryptocurrencies and company tickers in the London Stock Exchange (LSE), specifically, companies in the main and alternative market indices FTSE-100 and AIM-100. Heuristic-based as well as supervised classifiers are proposed and their advantages and drawbacks, including their ability to self-adapt to Twitter usage changes, are discussed. The experiment confirms a significant distortion in collected data when colliding or homonym cashtags exist, i.e., the same \$ acronym to refer to company tickers and cryptocurrencies. According to our results, the distinctive features of posts including cryptocurrencies or company tickers support accurate classification of colliding tweets (homonym cashtags) and Independent Models, as the most detached classifiers from training data, have the potential to be trans-applicability (in different stock markets) while retaining performance

STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this project, a novel approach is proposed using graph theory. This approach leverages Spatio- temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods