Using Long Term Implied Volatilities to Assess Past and Present U.S. Stock Prices

2019 ◽  
Author(s):  
Miguel Cantillo
Keyword(s):  
Stock Prices

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

العلاقة التبادلية بين سعر الصرف وأسعار الأسهم دراسة تطبيقية في سوق اسطنبول للأوراق المالية للمدة 2005-2020

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
سعدالله ألنعيمي
Keyword(s):  
Exchange Rate ◽  
Stock Prices ◽  
Negative Impact ◽  
Reciprocal Relationship ◽  
Nominal Exchange Rate ◽  
Share Price ◽  
Theoretical Interpretation ◽  
Short Term ◽  
The Exchange Rate

The study aims to analyzing the reciprocal relationship between the nominal exchange rate of the Turkish lira versus the U.S. dollar and the stock prices of the companies listed on the Istanbul Stock Exchange (ISE) expressed in the general market index for the period from 2005 to 2020 with 192 monthly observations, based on the traditional theory and the theory of portfolio balance model in theoretical interpretation for that relationship, aiming to identify the effect of the exchange rate on stock prices, as well as to analyze the causal relationship between those variables and to identify which of them is the cause or which is the result, using the Autoregressive Distributed Lag (ARDL) model. The research found that the exchange rate has a positive effect on stock prices in the long term, despite the emergence of the negative impact in the short term, but the long-term relationship has corrected the course of the short-term relationship with a time period not exceeding one month, in addition to proving that this relationship takes one direction. From the exchange rate towards stock prices, that is, the exchange rate is the reason and stock prices are the result, therefore the results of this research helps investors to predict future trends of stock prices depending on the exchange rate changes, and it also enables the companies, especially those with foreign transactions, to manage price risks the exchange rate in order to avoid its negative impact on its share price, as it represents an obstacle to achieving its main goal of maximizing the share price

scholarly journals Demonetisation and Indian Stock Market

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Ruchika Gahlot
Keyword(s):  
Linear Regression ◽  
Real Estate ◽  
Stock Prices ◽  
General Public ◽  
Financial Service ◽  
Medium Term ◽  
Donald Trump ◽  
Cash Transaction ◽  
Indian Stock Market

Demonetisation of 500 and 1000 bank notes was announced by PM Modi on 8th Nov 2016. There were number of speculation relating to its effects on general public and different sectors of Indian economy. This paper studies the effect of demonetisation on stock prices of different sectoral indices and Nifty listed on NSE by using t test, f test and linear regression. The results revealed that Nifty, automobiles, FMCG, Financial service, media and banking and real estate were major sectors affected by demonetisation decision as they are based on cash transaction. The prices of indices of NSE were influenced by S and P 500 in medium term and long term which may be the effect of policy of US president Donald Trump who was elected as President of U.S. on 8th November 2016.

scholarly journals ANALISIS HARGA SAHAM INDEX KOMPAS 100 DENGAN PENDEKATAN ERROR CORRECTION MODEL

2021 ◽  
Vol 4 (1) ◽  
pp. 406-414
Author(s):  
Amir Hamzah
Keyword(s):  
Error Correction ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Financial Statements ◽  
Error Correction Model ◽  
Short Term ◽  
Correction Model ◽  
Positive Effect

The purpose of this research is to analyze the short term and long term relationship between ROI, EPS, PER ,inflation, SBI, exchange rate,and GDP on Stock Price. The data in this research is company financial statements which included Compas 100 Index on the Indonesia Stock Exchange. statistical analysis in this research used stasionarity test, The Classical Assumptions Test, Cointegration Test, Error Correction Model Test. This research found that partially ROI, EPS, PER variables a positive effect on stock prices in the short term and long term, KURS and SBI a positive effect on stock prices in the short term, but there is no effect in the long term, inflation and GDP do not affect the stock price both in the short term and long term. Simultaneously affected the stock prices significantly affect on stock price both in the short term and long term.

Dynamic Modeling of Stock Market Interdependencies: An Empirical Investigation of Australia and the Asian NICs

2001 ◽  
Vol 04 (02) ◽  
pp. 235-264 ◽  
Author(s):  
Abul M. M. Masih ◽  
Rumi Masih
Keyword(s):  
Hong Kong ◽  
Stock Prices ◽  
Stock Markets ◽  
The Other ◽  
Substantial Contribution ◽  
Exogenous Shocks ◽  
Leading Role ◽  
Equilibrium Relationship ◽  
Short Run

This article examines the patterns of dynamic linkages among national stock prices of Australia and four Asian NIC stock markets namely, Taiwan, South Korea, Singapore and Hong Kong. By employing recently developed time-series techniques results seem to consistently suggest the relatively leading role of the Hong Kong market in driving fluctuations in the Australian and other NIC stock markets. In other words, given the generality of the techniques employed, Hong Kong showed up consistently as the initial receptor of exogenous shocks to the (long-term) equilibrium relationship whereas the Australian and the other NIC markets, particularly the Singaporean and Taiwanese markets had to bear most of the brunt of the burden of short-run adjustment to re-establish the long term equilibrium. Furthermore, given the dominance of the Hong Kong market in the region, the study also brings to light the substantial contribution of the Australian market in explaining the fluctuations to the other three markets, particularly Singapore and Taiwan. Finally, in comparison to all other NIC markets, Taiwan and Singapore appear as the most endogenous, with the former providing significant evidence of its short-term vulnerability to shocks from the more established market such as Australia.

scholarly journals Do the electricity price shocks influence the Sectoral Production and KSE100 Index in Pakistan? An ARDL structural breaks approach

2021 ◽  
Vol 5 (1) ◽  
pp. 17-46
Author(s):  
Abbas Khan ◽  
Muhammad Yar Khan ◽  
Abdul Qayyum Khan
Keyword(s):  
Stock Prices ◽  
Structural Breaks ◽  
Price Change ◽  
Equity Market ◽  
Electricity Price ◽  
Price Shocks ◽  
Cross Sectional ◽  
Industrial Sectors ◽  
Bound Test

This research investigates the long-term cointegration of electricity price with sectoral production and equity market in Pakistan. Fourteen major industrial sectors and the KSE100 index is taken into consideration to determine the relationship. Literature in this regard is available but this research is distinct from previous literature for it tests the sectoral production and equity market relationship with electricity price change in Pakistan. Monthly data from 1st Jan 2011 till 31st Dec 2019 is taken for fourteen sectors from the sources of Quantum Index Pakistan Bureau of Statistics (PBS) and for KSE100 index from (www.investing.com). An Auto Regressive Distributed Lag (ARDL) model and bound test for multiple structural breaks has been applied. It is found that almost the production of all industrial sectors and KSE100 index stock prices are adversely affected by the electricity price shocks both in long-term and short-term. The study suggests that management should implement a moderate monitory policy that is neither more expansionary nor contractionary. The government should provide incentives to those who successfully control energy wastage. A mixed kind of energy policy is recommended with higher weightage to the development of renewable energies to reduce foreign exchange outflow with imported furnace oil. This study is limited to the sectoral production and equity market of Pakistan. A cross-sectional research is encouraged to compare the connection between major energy costs and macroeconomic variables in different countries.

scholarly journals Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3319
Author(s):  
Varun Dogra ◽  
Aman Singh ◽  
Sahil Verma ◽  
Abdullah Alharbi ◽  
Wael Alosaimi
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Research Work ◽  
Abnormal Returns ◽  
Second Phase ◽  
Financial News ◽  
News Events ◽  
News Event ◽  
The Impact

Machine learning has grown in popularity in recent years as a method for evaluating financial text data, with promising results in stock price projection from financial news. Various research has looked at the relationship between news events and stock prices, but there is little evidence on how different sentiments (negative, neutral, and positive) of such events impact the performance of stocks or indices in comparison to benchmark indices. The goal of this paper is to analyze how a specific banking news event (such as a fraud or a bank merger) and other co-related news events (such as government policies or national elections), as well as the framing of both the news event and news-event sentiment, impair the formation of the respective bank’s stock and the banking index, i.e., Bank Nifty, in Indian stock markets over time. The task is achieved through three phases. In the first phase, we extract the banking and other co-related news events from the pool of financial news. The news events are further categorized into negative, positive, and neutral sentiments in the second phase. This study covers the third phase of our research work, where we analyze the impact of news events concerning sentiments or linguistics in the price movement of the respective bank’s stock, identified or recognized from these news events, against benchmark index Bank Nifty and the banking index against benchmark index Nifty50 for the short to long term. For the short term, we analyzed the movement of banking stock or index to benchmark index in terms of CARs (cumulative abnormal returns) surrounding the publication day (termed as D) of the news event in the event windows of (−1,D), (D,1), (−1,1), (D,5), (−5,−1), and (−5,5). For the long term, we analyzed the movement of banking stock or index to benchmark index in the event windows of (D,30), (−30,−1), (−30,30), (D,60), (−60,−1), and (−60,60). We explore the deep learning model, bidirectional encoder representations from transformers, and statistical method CAPM for this research.

scholarly journals Forecasting stock market with neural networks

2021 ◽  
Author(s):  
Hua Jiang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Economic Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Nonlinear Problems ◽  
Neural Network Approach ◽  
One Year ◽  
Long Term Trend

The objective of this project is to use neural networks to forecast next day's stock closing price. In the past, researchers used different methods to forecast stock price such as technical analysis, fundamental analysis, and economic analysis. Forecasting stock prices is a problem that has been usually approached in terms of weekly, monthly, or quarterly forecast. This project aims at finding a feasible way, by using neural networks, to make daily forecasts. Most methods proposed so far, such as technical, fundamental and economic analysis, are limited to solving the problem as a long term trend analysis. Thus, these methods either lack accuracy or add extra expenses to the forecasting task, especially if a company's fundamental statistics are out of date. Therefore it is difficult to forecast day-to-day close price as a nonlinear problem. In this study, three portfolios are created. Portfolio #1 is based on subjective forecasts, Portfolio #2 uses a neural network to forecast, and Portfolio #3 using CAPM optimizer forecast. A comparison of these portfolios showed that the CAPM optimization based on neural network forecast (Portfolio #3) achieved the highest return. The degree of accuracy is compared in three economic periods: the beginning of recession; the middle of recession; and the beginning of recovery. Stock forecasting example cases are given to illustrate this neural network approach to solve nonlinear problems. It is observed, indeed, that next day closing prices are forecast with better accuracy within a one-year period than other methods.

Sign in / Sign up

Export Citation Format

Share Document