Predicting the Effects of News Sentiments on the Stock Market

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.Comment: 4 page

Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis

This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bidirectional LSTM) BiLSTM model which is a variation of the LSTM model to predict stock closing price. The stock prediction is applied to a selected company from the Indonesian stock market using historical prices. The model is then evaluated using metrics Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). A graphical comparison between the actual price and predicted price of the stock is charted to study the stock price movement. To study the impact during COVID-19 on the stock prices, an intervention analysis is conducted along with the Wilcoxon model. The stock price prediction model can forecast the price of stocks before and during the financial crisis with minimal error. The intervention analysis result showed that health sectors have a positive effect while other sectors such as transportation, finance, information technology, and entertainment have a negative effect during the financial crisis of COVID-19. Being able to analyze and study the stock price movement of stocks is beneficial to investors in understanding the impact of the financial crisis on some industries and the behavior of certain stocks or industries under the circumstances which can lead to alternate investment strategies and decision making

PENGARUH PREDIKSI FINANCIAL DISTRESS TERHADAP HARGA SAHAM DENGAN STRUKTUR MODAL SEBAGAI VARIABEL INTERVENING : Studi Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia Periode 2011-2015

Tujuan penelitian ini adalah untuk mengetahui bagaimana pengaruh prediksi financial distress terhadap harga saham, pengaruh prediksi financial distress terhadap struktur modal, pengaruh struktur modal terhadap harga saham, serta pengaruh prediksi financial distress terhadap harga saham melalui struktur modal pada perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia. Metode penelitian yang digunakan dalam penelitian ini adalah metode deskriptif. Populasi pada penelitian ini adalah perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia periode tahun 2011 sampai dengan 2015, sedangkan untuk pengambilan sampel menggunakan metode purposive sampling. Data yang digunakan adalah data sekunder yang dikumpulkan dengan teknik dokumentasi. Financial distress dalam penelitian ini diukur menggunakan model prediksi Grover (2010), struktur modal diukur menggunakan Debt to Equity Ratio, dan harga saham diukur menggunakan harga saham penutupan rata-rata. Dalam menganalisis data, penelitian ini menggunakan pengujian statistik analisis jalur (path analysis). Berdasarkan perhitungan analisis jalur dengan bantuan software Eviews 8.1 menghasilkan bahwa prediksi financial distress berpengaruh terhadap harga saham, prediksi financial distress berpengaruh terhadap struktur modal, struktur modal berpengaruh terhadap harga saham, dan penelitian ini menunjukkan bahwa struktur modal dapat menjadi penunjang untuk menjelaskan pengaruh financial distress terhadap harga saham. ;--- The purpose of this study was to determine how the influence financial distress prediction to stock price, the influence financial distress prediction to capital structure, the influence capital structure to stock price, and the influence financial distress prediction to stock price through capital structure in manufacturing companies listed in Indonesia Stock Exchange. The method used in this study is the descriptive method. The population in this research is manufacturing companies listed in Indonesia Stock Exchange from 2011 to 2015, while for sampling using purposive sampling method. The data used are secondary data collected by technical documentation. Financial distress in this research measured by model prediction Grover, capital structure measured by Debt to Equity Ratio, and stock price measured by closing price average. In analyzing the data, this study used path analysis tests. Based on the calculation of path analysis with the help of software Eviews 8.1 produce that financial distress prediction effect to stock price, financial distress prediction effect to capital structure, capital structure effect to stock price, and this research produce that capital structure can be a supporting to describe the influence of financial distress prediction toward stock price

Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (APST), which is a two step approach to predict the direction of change of stock price indices. First, performs data approximation by using the technique called Multilevel Segment Mean (MSM). In second phase, prediction is performed for the approximated data using Euclidian distance and Nearest-Neighbour technique. The computational cost of data approximation is O(n ni) and computational cost of prediction task is O(m |NN|). Thus, the accuracy and the time required for prediction in the proposed method is comparatively efficient than the existing Label Based Forecasting (LBF) method [1].Comment: 11 page

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PD

Teknik Jaringan Syaraf Tiruan Feedforward Untuk Prediksi Harga Saham Pada Pasar Modal Indonesia

To predict the condition of stock price, several technical analysis models have been used and expanded such as MACD, Fourier Transform, Accumulator Swing Index , Stochastic Oscillator etc. For input they are using the various prices such as Open, high, low , close , volume, BID, ASK price, and the output is a graphic that shows the decision whether to sell, buy or hold. Another method to determine the stock price by using Fundamental Analysis method. Fundamental method is an analysis that is based on the ratio or financial report from the existing company. Neural Network System Technology has been implemented in various applications especially in introduce the pattern. This power has attracted several people to use Neural Network for medical, Finance, Investment and marketing. Assuming that the prediction of the output system (next output prediction) is deterministic, than the suitable N.N model to predict it is Feed Forward. The prediction of the stock price is the complex interaction between unstable market and unknown random processes factor. The data from stock price can be determined by time series. If we have daily data from a certain period, for example : Xt(t = 1,2,...) than the stock price for the next period (t+h) can be predicted (the timing used can be in hourly, daily, weekly, monthly or yearly). To get the good prediction, the inputs from several aspects of the share prices have to be input in Neural Network after that the weighing principal can be adapted to minimize the wrong prediction in the first future steps. By using the final weighing, an action is done to done to minimize the total error in the second future steps. Due to that, the risk of Investor's decision to sell or buy the stock can be minimized. This paper will discuss on how to use and implement Time Series Neural Network to predict the stock market in Semen Gresik (SMGR) and Gudang Garam (GGRM

THE DESIGN OF A NETWORK-BASED MODEL FOR BUSINESS PERFORMANCE PREDICTION

While much research work has been devoted to analysis and prediction of individuals’ behavior in social networks, very few studies about the analysis of business networks are conducted. Empowered by recent research on automated mining of business networks, this paper illustrates the design of a novel business network-based model called Energy Cascading Model (ECM) for the analysis and prediction of business performance using the proxies of stock prices. More specifically, the proposed prediction model takes into account both influential business relationships and twitter sentiments of firms to infer their stock price movements. Our empirical experiments based on a publicly available financial corpus and social media postings reveal that the proposed ECM model is effective for the prediction of directional stock price movements. The business implication of our research is that business managers can apply our design artifacts to more effectively analyze and predict the potential business performance of targeted firms