scholarly journals With a Bang, not a Whimper: Pricking Germany's “Stock Market Bubble” in 1927 and the Slide into Depression

2003 ◽  
Vol 63 (1) ◽  
pp. 65-99 ◽  
Author(s):  
Hans-Joachim Voth
Keyword(s):  
Stock Market ◽  
Central Bank ◽  
Stock Prices ◽  
Money Market ◽  
Short Term ◽  
Real Effects ◽  
Foreign Borrowing ◽  
Relative Stabilization

In May 1927, the German central bank intervened indirectly to reduce lending to equity investors. The crash that followed ended the only stock market boom during Germany's relative stabilization 1924–1928. The evidence strongly suggests that the German central bank under Hjalmar Schacht was wrong to be concerned about stock prices—there was no bubble. Also, the Reichsbank was mistaken in its belief that a fall in the market would reduce the importance of short-term foreign borrowing and improve conditions in the money market. The misguided intervention had important real effects. Investment suffered, helping to tip Germany into depression.

Prior Return Effect in Indian Stock Market: An Intra-day Analysis

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Vanita Tripathi ◽  
Shalini Aggarwal
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
High Frequency ◽  
Good News ◽  
High Frequency Data ◽  
Short Term ◽  
Fund Managers ◽  
News Events ◽  
Return Effect ◽  
Indian Stock Market

In a first of this kind, this paper examines the issue of prior return effect in Indian stock market in intra-day analysis using high frequency data. We document that in Indian stock market, security returns exhibit a reversal in their direction within few minutes of extreme price rises as well as price falls. However the speed with which the correction takes place is slightly different for good news events and bad news events. Indian investors tend to be optimistic as they immediately bring stock prices up following unjustified price falls but take time to bring stock prices down following unjustified price rises. These findings lend a further support to short-term overreaction literature. More importantly, these findings serve as a proof of predictability of the direction of future stock prices and consequent returns on an intra-day basis. It forwards important investment implications for traders, fund managers, and investors at large.

scholarly journals Stock Prediction Using Sentiment Analysis and LSTM

2021 ◽  
Vol 9 (9) ◽  
pp. 1752-1756
Author(s):  
Ms. Anjima K. S
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Programming Language ◽  
Stock Prices ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Python Programming Language ◽  
Long Short Term Memory ◽  
Python Programming

Abstract: The stock market is a difficult area to anticipate since it is influenced by a variety of variables at the same time. The stock exchange is where equities are exchanged, transferred, and circulated. This research proposes a hybrid algorithm that predicts a stock's next day closing prices using sentiment analysis and Long Short Term Memory. The LSTM model seems to be quite popular in time-series forecasting, which is why it was selected for this project. Our proposed methodology makes use of the temporal association between public opinion and stock prices. Part-of-speech tagging is used to do sentiment analysis, and Long Short Term Memory is utilized to predict the stock's next day closing price. When these two factors are combined, we get a good picture of the stock's future. In this project, two main datasets have been used: HCLTECH company stock data and the news related to each stock of the HCL company for each day. The project is implemented by using the python programming language. The python programming language has been used to execute the project. This also incorporates machine learning along with public feedback. Sentiment analysis enables us to evaluate a diversity of political and economic factors, which have a significant impact on the stock market. Keywords: LSTM, sentiment analysis, RNN, Back propagation neural network.

scholarly journals Systematic optimization of Long Short-Term Memory model for predicting NYSE Arca Airline Index (XAL) during COVID-19

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah Dong ◽  
Amber Wang
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Airline Industry ◽  
Stock Price ◽  
Short Term Memory ◽  
The United States ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Long Short Term Memory

Predicting stock prices has been both challenging and controversial. Since it first spread through the United States, the COVID-19 pandemic has impacted the stock market in a multitude of ways. Thus, stock price prediction has become even more challenging. Recurrent neural networks (RNN) have been widely used in many fields to predict financial time series. In this study, Long Short-Term Memory (LSTM), a special form of RNN, is used to predict the stock market direction for the US airline industry by using NYSE Arca Airline Index (XAL). The LSTM model was optimized through changing different hyperparameters of the model architecture to find the best combination for increased accuracy and performance evaluated by several metrics, including raw RMSE (3.51) and MAPA (4.6%), and very high MAPA (95.4%) and R^2 (0.978).

Dynamic Relationship Between Stock Market Sector Indices and Macroeconomic Variables in India

2019 ◽  
Vol 1 (2) ◽  
pp. 31-41
Author(s):  
Neeru Gupta ◽  
Ashish Kumar
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Positive Relationship ◽  
Short Term ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Market Sector ◽  
Long Run ◽  
Integration Test

This study investigates the long-term and short-term relationships between selected macroeconomic variables and the selected Indian stock market sector indices over the period of 2010 to 2017. The Johansen Co-integration Test, the Vector error correction model (VECM), is applied to calculate the long-term and short-term relationship between sector indices and macroeconomic variables. It is found that stock prices are exposed to macroeconomic factors, but the level of sensitivity is different in different sectors. Out of five sectors taken in the study, it is found that only the realty sector has long run relationship with macroeconomic variables. Other sectors have no long run relationship with macroeconomic variables. Along with this, it is also found that the Auto index has a significant short-term positive relationship with gold prices and the FMCG sector index has a significant short-term positive relationship with industrial production. The consumer price index and exchange rate have significant short run relationship with realty sector index.

scholarly journals A stylized macro-model with interacting real, monetary and stock markets

2021 ◽  
Author(s):  
F. Cavalli ◽  
A. Naimzada ◽  
N. Pecora
Keyword(s):  
Stock Market ◽  
Interest Rates ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Market Integration ◽  
Dynamic Properties ◽  
National Income ◽  
Money Market ◽  
The Business Cycle

AbstractWe propose a model economy consisting of interdependent real, monetary and stock markets. The money market is influenced by the real one through a standard LM equation. Private expenditures depend on stock prices, which in turn are affected by interest rates and real profits, as these contribute to determine the participation level in the stock market. An evolutionary mechanism regulates agents’ participation in the stock market on the basis of a fitness measure that depends on the comparison between the stock return and the interest rate. Relying on analytical investigations complemented by numerical simulations, we study the economically relevant static and dynamic properties of the equilibrium, identifying the possible sources of instabilities and the channels through which they spread across markets. We aim at understanding what micro- and macro-factors affect the dynamics and, at the same time, how the dynamics of asset prices, which are ultimately influenced by the money market, behave over the business cycle. Starting from isolated markets, we show the effect of increasing the market interdependence on the national income, the stock price and the share of agents that participate in the stock market at the equilibrium. Moreover, we investigate the stabilizing/destabilizing role of market integration and the possible emergence of out-of-equilibrium dynamics.

scholarly journals A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Alireza Namdari ◽  
Tariq S. Durrani
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Hybrid Model ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term ◽  
Self Organizing Maps ◽  
Hidden Layer ◽  
Low Dimensional ◽  
Nonlinear Autoregressive

AbstractStock market prediction is important for investors seeking a return on the capital invested, though this prediction is a challenging task, due to the complexity of stock price time-series. This task can be performed by conducting two primary analyses: fundamental and technical. In this paper, we examine the predictability of these two analyses using a multilayer feedforward perceptron neural network (MLP) and determine whether MLP is capable of accurately predicting stock market short-term trends. We utilize stock prices (2013 Mar – 2018 Jun) and twelve financial ratios of technology companies selected through a feature selection preprocess. Our model uses self-organizing maps (SOMs) for clustering the historical prices and produces a low-dimensional discretized representation of the input space. The best results are obtained through hyper-parameter optimizations using a three-hidden layer MLP. The models are integrated using a nonlinear autoregressive structure with exogenous input (NARX). We find that the hybrid model successfully predicts the short-term stock trends. The hybrid model yields the greatest directional accuracy (70.36%) as compared to fundamental and technical analyses (64.38% and 62.85%) and state-of-the-art models. The results indicate that the market is not fully efficient. Our model will be useful to practitioners seeking investing and trading opportunities and others interested in the study of financial markets.

scholarly journals Buy The Rumor, Sell The News! What About Takeover Rumors?

2015 ◽  
Vol 32 (1) ◽  
pp. 143 ◽  
Author(s):  
Mhammed Laouiti Laouiti ◽  
Badreddine Msolli ◽  
Aymen AJINA Ajina
Keyword(s):  
Stock Market ◽  
Web Sites ◽  
Stock Prices ◽  
Short Term ◽  
Average Return ◽  
News Agencies ◽  
The Media ◽  
Three Components

<p class="Texte">This paper attempts to quantify the short-term impact of takeover rumors on target stock prices. The study was conducted on the French stock market between 1997 and 2011 and concerns 200 rumors that appeared in the media (news agencies, newspapers, and Web sites). Our results show that this particular kind of information has a significant impact on the prices of target companies around and after the date of rumor appearance. The best performance of target shares is observed 50 days after the dissemination of the rumors in the media, with an average return of 4%. This performance is mainly explained by three components:  credibility, rumor characteristics, and the anticipated effects of the takeover bid. </p>

scholarly journals Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

2021 ◽  
Vol 18 (2) ◽  
pp. 401-418
Author(s):  
Ming-Che Lee ◽  
Jia-Wei Chang ◽  
Jason Hung ◽  
Bae-Ling Chen
Keyword(s):  
Stock Market ◽  
Capital Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Short Term ◽  
Continuous Growth ◽  
Technical Indicators ◽  
Deep Network ◽  
The Capital Market

The sustainable development of the national economy depends on the continuous growth and growth of the capital market, and the stock market is an important factor of the capital market. The growth of the stock market can generate a huge positive force for the country's economic strength, and the steady growth of the stock market also plays a pivotal role in the overall economic pulsation and is very helpful to the country's high economic development. There are different views on whether the technical analysis of the stock market is efficient. This study aims to explore the feasibility and efficiency of using deep network and technical analysis indicators to estimate short-term price movements of stocks. The subject of this study is TWSE 0050, which is the most traded ETF in Taiwan's stock exchange, and the experimental transaction range is 2017/01 ~ 2019 Q3. A four layer Long Short-Term Memory (LSTM) model was constructed. This research uses well-known technical indicators such as the KD, RSI, BIAS, Williams% R, and MACD, combined with the opening price, closing price, daily high and low prices, etc., to predict the trend of stock prices. The results show that the combination of technical indicators and the LSTM deep network model can achieve 83.6% accuracy in the three categories of rise, fall, and flatness.

scholarly journals Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongying Zheng ◽  
Hongyu Wang ◽  
Jianyong Chen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Optimal Solution ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Memory Network ◽  
Long Short Term Memory

As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.

scholarly journals Stock Market Prediction Using LSTM

2021 ◽  
pp. 123-128
Author(s):  
Ishwarappa Kalbandi ◽  
Ashutosh Jare ◽  
Om Kale ◽  
Himanshu Borole ◽  
Swapnil Navsare
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Stock Index ◽  
Short Term ◽  
Neural Network Approach ◽  
Term Memory ◽  
Live Stock ◽  
Long Short Term Memory

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development of word vector in deep learning, we demonstrate the concept of “stock vector.” The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network. The experimental results show that the deep LSTM with embedded layer is better.

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