scholarly journals Information Processing and Financial Market Price Adjustments

2019 ◽  
Vol 09 (07) ◽  
pp. 2337-2353
Author(s):  
Edwin H. Neave ◽  
William R. Scott
Keyword(s):  
Information Processing ◽  
Financial Market ◽  
Market Price ◽  
Price Adjustments

Conviction Narrative Theory and Understanding Decision-Making in Economics and Finance

2018 ◽  
Author(s):  
David Tuckett
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Financial Market ◽  
Narrative Theory ◽  
Economic Outcomes ◽  
Animal Spirits ◽  
Money Managers ◽  
Radical Uncertainty ◽  
Human Capacity ◽  
Market Instability

This chapter describes the radically uncertain context faced by money managers and how they cope by developing conviction narratives. It then generalizes these findings to introduce a wider theory of decision-making under radical uncertainty, termed Conviction Narrative Theory (CNT). CNT differs from standard approaches to decision-making in economics and behavioural psychology that are limited to theories of efficient and inefficient information processing in contexts where data is available to calculate future probabilities. In radical uncertainty, we cannot know which bits of information are useful. CNT explains the human capacity to cope despite this situation: actors organize their experience through narratives and use the emotions attaching to them to feel the conviction to act. In effect, CNT operationalizes Keynes’ (1936) formulation of animal spirits as a human solution to radical uncertainty; and it provides more plausible and empirically substantiated microfoundations on which to build understanding of aggregate economic outcomes, the development of monocultures, and financial market instability.

REPLICATION OF NON-TRIVIAL DIRECTIONAL MOTION IN MULTI-SCALES OBSERVED BY THE RUNS TEST

2012 ◽  
Vol 16 ◽  
pp. 136-148 ◽  
Author(s):  
YOSHIHIRO YURA ◽  
TAKAAKI OHNISHI ◽  
KENTA YAMADA ◽  
HIDEKI TAKAYASU ◽  
MISAKO TAKAYASU
Keyword(s):  
Financial Market ◽  
Time Scale ◽  
Market Price ◽  
Short Time Scale ◽  
Price Model ◽  
Tuning Parameters ◽  
Multi Scale ◽  
Directional Motion ◽  
Short Time ◽  
Multi Scales

Non-trivial autocorrelation in up-down statistics in financial market price fluctuation is revealed by a multi-scale runs test(Wald-Wolfowitz test). We apply two models, a stochastic price model and dealer model to understand this property. In both approaches we successfully reproduce the non-stationary directional price motions consistent with the runs test by tuning parameters in the models. We find that two types of dealers exist in the markets, a short-time-scale trend-follower and an extended-time-scale contrarian who are active in different time periods.

A Study on Risk Management in Financial Market

2019 ◽  
pp. 182-197
Author(s):  
Smruti Rekha Das ◽  
Kuhoo ◽  
Debahuti Mishra ◽  
Pradeep Kumar Mallick
Keyword(s):  
Risk Management ◽  
Financial Markets ◽  
Interest Rates ◽  
Financial Market ◽  
Stock Prices ◽  
Historical Data ◽  
Market Price ◽  
Business Goals ◽  
Current Price ◽  
Management Of Risk

The basic aim of risk management is to recognize, assess, and prioritize risk in order to assure that the uncertainty should not deviate from the intended purpose of the business goals. Risk can take place from various sources, which includes uncertainty in financial markets, recessions, inflation, interest rates, currency fluctuations, etc. Various methods used for this management of risk are faced with various decisions such as the market price, historical data, statistical methodologies, etc. For stock prices, the information derives from the historical data where the next price depends only upon the current price and some of the outside factors. Financial market is very risky to invest money, but the proper prediction with handling the risk will benefit a lot. Various types of risk in the financial market and the appropriate solutions to overcome the risk are analyzed in this study.

Disequilibrium Trade in a Large Market for an Indivisible Good

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Luis C. Corchón ◽  
José Rueda-Llano
Keyword(s):  
Market Price ◽  
Price Adjustment ◽  
Indivisible Good ◽  
Speed Of Adjustment ◽  
Large Market ◽  
Incentive Compatible ◽  
Current Period ◽  
Price Adjustments ◽  
Arbitrage Opportunities ◽  
Social Surplus

AbstractDisequilibrium trade can occur in a market lacking both recontracting and a computational system that maps utilities into prices. This paper studies disequilibrium trade in a large market for an indivisible good. We focus on the possible speed of adjustment when arbitrage among periods is feasible and the surplus loss. We find that incentive compatible sequential trade through a disequilibrium path is only compatible with sluggish price adjustments and sufficiently impatient agents. Thus, price adjustment does not depend on excess demand alone but on arbitrage opportunities and the willingness of agents to engage on them. We find that the upper bound on the speed of price adjustment involves a lower bound for the social surplus loss, whatever the kind of rationing. The reason is that even when the market price converges to the surplus maximizing value, as it happens when rationing is efficient, some pieces of surplus are not attainable at the current period due to arbitrage. Moreover, faster price adjustments do not imply less surplus loss, because the effect of price changes on transactions via arbitrage. Finally, under weaker-than-efficient rationing there is a one period incentive compatible trading procedure in which most of the surplus is destroyed. The procedure has the property that almost every agent in the market trades.

scholarly journals Optimizing Stock Market Prediction using Long Short Term Memory

2019 ◽  
Vol 9 (1) ◽  
pp. 2819-2823
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Market Price ◽  
Data Set ◽  
Stock Market Prediction ◽  
Company Stock ◽  
Financial Gain ◽  
And Mathematics

Stock market prediction has been an important issue in the field of finance, engineering and mathematics due to its potential financial gain. Stock market prediction is a process of predicting the future value of a company stock or other financial instrument traded in financial market. Stock market prediction brings with it the challenge of proving whether the financial market is predictable or not, since stock market data is of high velocity. This project proposes a machine learning model to predict stock market price based on the data set available by using LSTM model for performing prediction by de-noising the data using wavelet transform and performing auto-encoding on the data. The process includes removal of noise, preprocessing, feature selection, data mining, analysis and derivations. This project focuses mainly on the use of LSTM algorithm along with a layer of neural network to forecast stock prices and allocate stocks to maximize the profit within the risk factor range of the stock buyers and sellers.

PARTIAL EQUILIBRIUM AND MARKET COMPLETION

2005 ◽  
Vol 08 (04) ◽  
pp. 483-508 ◽  
Author(s):  
YING HU ◽  
PETER IMKELLER ◽  
MATTHIAS MÜLLER
Keyword(s):  
Financial Market ◽  
Market Price ◽  
External Source ◽  
Partial Equilibrium ◽  
Individual Risk ◽  
Market Price Of Risk ◽  
Price Of Risk ◽  
Incomplete Financial Market ◽  
External Risk ◽  
The One

We consider financial markets with agents exposed to an external source of risk which cannot be hedged through investments on the capital market alone. The sources of risk we think of may be weather and climate. Therefore we face a typical example of an incomplete financial market. We design a model of a market on which the external risk becomes tradable. In a first step we complete the market by introducing an extra security which valuates the external risk through a process parameter describing its market price. If this parameter is fixed, risk has a price and every agent can maximize the expected exponential utility with individual risk aversion obtained from his risk exposure on the one hand and his investment into the financial market consisting of an exogenous set of stocks and the insurance asset on the other hand. In the second step, the market price of risk parameter has to be determined by a partial equilibrium condition which just expresses the fact that in equilibrium the market is cleared of the second security. This choice of market price of risk is performed in the framework of nonlinear backwards stochastic differential equations.

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