On Feedforward Stock Trading Control Using a New Transaction Level Price Trend Model

2021 ◽  
pp. 1-1
Author(s):  
B. Ross Barmish ◽  
James A. Primbs ◽  
Sean Warnick
Keyword(s):  
Stock Trading ◽  
Price Trend ◽  
Trend Model ◽  
Transaction Level

A Mixture Price Trend Model for Long-Term Risk Management

2010 ◽  
pp. 157-173
Author(s):  
Eric S. Fung ◽  
Wai-Ki Ching ◽  
Tak-Kuen Siu
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Estimation Method ◽  
Real Data ◽  
Price Trend ◽  
Trend Model ◽  
Proposed Model ◽  
Price Trends ◽  
Long Term Forecasting

In financial forecasting, a long-standing challenging issue is to develop an appropriate model for forecasting long-term risk management of enterprises. In this chapter, using financial markets as an example, we introduce a mixture price trend model for long-term forecasts of financial asset prices with a view to applying it for long-term financial risk management. The key idea of the mixture price trend model is to provide a general and flexible way to incorporate various price trend behaviors and to extract information from price trends for long-term forecasting. Indeed, the mixture price trend model can incorporate model uncertainty in the price trend model, which is a key element for risk management and is overlooked in some of the current literatures. The mixture price trend model also allows the incorporation of users’ subjective views on long-term price trends. An efficient estimation method is introduced. Statistical analysis of the proposed model based on real data will be conducted to illustrate the performance of the model.

Intelligent stock trading system with price trend prediction and reversal recognition using dual-module neural networks

1993 ◽  
Vol 3 (3) ◽  
pp. 225-248 ◽  
Author(s):  
Gia-Shuh Jang ◽  
Feipei Lai ◽  
Bor-Wei Jiang ◽  
Tai-Ming Parng ◽  
Li-Hua Chien
Keyword(s):  
Neural Networks ◽  
Trend Prediction ◽  
Trading System ◽  
Stock Trading ◽  
Price Trend ◽  
Dual Module

Forecasting and trading strategies based on a price trend model

2000 ◽  
Vol 19 (6) ◽  
pp. 485-498 ◽  
Author(s):  
Josephine W. C. Kwan ◽  
K. Lam ◽  
Mike K. P. So ◽  
Philip L. H. Yu
Keyword(s):  
Trading Strategies ◽  
Price Trend ◽  
Trend Model

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

What's Happening to Profits at Cazenovia Creations?

2013 ◽  
Vol 8 (1) ◽  
pp. 24-29 ◽  
Author(s):  
Margaret Garnsey ◽  
Andrea Hotaling
Keyword(s):  
Financial Analysis ◽  
Financial Statements ◽  
Internal Controls ◽  
Net Income ◽  
Database Query ◽  
Microsoft Access ◽  
Level Data ◽  
Query Tool ◽  
Transaction Level

ABSTRACT In this case, students assume the role of an accounting professional asked by a client to investigate why net income is not as strong as expected. The students must first analyze a set of financial statements to identify areas of possible concern. After determining the areas to investigate, the students use a database query tool to see if they can determine causes by examining transaction level data. Finally, the students are asked to professionally communicate their findings and recommendations to their client. The case provides students with experience in using query-based approaches to answering business questions. It is appropriate for students with basic query and financial analysis skills and knowledge of internal controls. A Microsoft Access database with transaction details for the final seven months of the current year as well as financial statements for the current and prior year are provided.

How Smooth Is Price Discovery? Evidence from Cross-listed Stock Trading

2012 ◽  
Author(s):  
Haiqiang Chen ◽  
Paul Moon Sub Choi ◽  
Yongmiao Hong
Keyword(s):  
Price Discovery ◽  
Stock Trading

Investigation of a New Approach to Predict Water Quality Extremes with a Case Study of Chemical Determinands in Stream Water

1991 ◽  
Vol 24 (6) ◽  
pp. 25-33
Author(s):  
A. J. Jakeman ◽  
P. G. Whitehead ◽  
A. Robson ◽  
J. A. Taylor ◽  
J. Bai
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Probability Distributions ◽  
Management Strategies ◽  
Hybrid Modelling ◽  
Trend Model ◽  
Environmental Extremes ◽  
Modelling Approach

The paper illustrates analysis of the assumptions of the statistical component of a hybrid modelling approach for predicting environmental extremes. This shows how to assess the applicability of the approach to water quality problems. The analysis involves data on stream acidity from the Birkenes catchment in Norway. The modelling approach is hybrid in that it uses: (1) a deterministic or process-based description to simulate (non-stationary) long term trend values of environmental variables, and (2) probability distributions which are superimposed on the trend values to characterise the frequency of shorter term concentrations. This permits assessment of management strategies and of sensitivity to climate variables by adjusting the values of major forcing variables in the trend model. Knowledge of the variability about the trend is provided by: (a) identification of an appropriate parametric form of the probability density function (pdf) of the environmental attribute (e.g. stream acidity variables) whose extremes are of interest, and (b) estimation of pdf parameters using the output of the trend model.

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