USE OF QUANTITATIVE RATIOS FROM THE SYSTEM OF NATIONAL ACCOUNTS IN PREDICTING PRICE BUBBLES ON THE HOUSING MARKET

One of the main causes of the economic and sovereign debt crisis in 2010 – 2012 in some European countries like the United Kingdom, Spain and Ireland was the bursting of the residential market price bubble that was formed in the previous period. In this paper, a specific methodology of indicator analysis of the System of National Accounts and other data has been analyzed if it can help identify and prevent forming of some possible future price bubbles at the residential market, and therefore negative macroeconomic consequences of their bursting. Comparative indicator analysis and critical values suggest measurements of excessive construction activity that led to forming of price bubbles on the residential market. Econometric analysis has shown that it is not possible to establish critical values as variable of interest is not statistically significant.

Securities Analysis and Portfolio Management of Selected NSE Securities

“Technical Analysis is the study of data generated by the action of markets and by the behaviour and psychology of market participants and observers”: -Constitution of the market technicians Association Technical analysis is a completely different approach to stock market investing- it doesn’t try to find the intrinsic value of a company or try to find whether a share is mispriced or undervalued. "Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends. “A technical analyst is interested only in the price movements in the market. So, it is all about analysing the demand and supply or a price volume analysis. Technical analysis considers only the actual price behaviour of the market or instrument, based on the premise that price reflects all relevant factors before an investor becomes aware of them through other channels. These stock market indicators would help the investor to identify major market turning points. This paper examines the technical analysis of selected companies which helps to understand the price behaviour of the shares, the signals given by them and to assist investment decisions in the Indian stock Market.

ARBITRAGEUR BEHAVIOR IN SENTIMENT-DRIVEN ASSET-PRICING

This study aims to model arbitrageur behavior in a sentiment-driven capital asset-pricing model under the premise of reflecting a more detailed decomposition of investor types in the equity markets. We explore the behavior and the impact of arbitrageur behavior, particularly, on pricing and on key financial ratios. We observe that the prevalence of the arbitrageur counteracts the effects of unsophisticated investors, resulting in a lower volatility of the price–dividend ratio, lower predictive power of changes in consumption for future price changes and lower equity premium. Thus, the results of our research allow us to conjecture that the extrapolation bias in the prices is lowered.

Durable Goods Adoption and the Consumer Discount Factor: A Case Study of the Norwegian Book Market

We exploit a change in Norway’s fixed book pricing policies to construct exclusion restrictions with which to identify consumers’ discount factor. We assume that the policy change generated an unanticipated, exogenous shock to consumers’ expectations about future price cuts. Our findings suggest that consumers are much more impatient than would be implied by the real rate of interest, challenging the standard assumed rate of discounting in the extant literature on dynamic demand estimation. The high rate of consumer impatience is consistent with laboratory studies in the behavioral economics and decision-making literatures. This paper was accepted by Matthew Shum, marketing.

The impact of “4 + 7” volume-based drug procurement on the volume, expenditures, and daily costs of antihypertensive drugs in Shenzhen, China: an interrupted time series analysis

Background In 2019, Chinese government launched a nationwide volume-based drug procurement aiming at reducing drug prices and saving drug costs through economies of scale, which aroused widespread attention. The first round of the policy pilot was implemented in 4 municipalities and 7 sub-provincial cities, referred to as “4 + 7” policy. In the “4 + 7” policy, 7 antihypertensive drugs were included. This study was conducted to evaluate the impact of “4 + 7” policy on the use of policy-related antihypertensive drugs. Method This study applied single-group Interrupted Time Series (ITS) design. We used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, covering 24 months from January 2018 to December 2019. Antihypertensive drugs related to “4 + 7” policy were selected as study samples, including 7 drugs in the “4 + 7” List and 17 alternative drugs. Alternative drugs refer to antihypertensive drugs that have an alternative relationship with “4 + 7” List drugs in clinical use and have not yet been covered by the policy. “4 + 7” List drugs were then divided into bid-winning and bid-non-winning products according to the bidding results. Purchase volume, expenditures, and daily costs were selected as outcome variables, and were measured using Defined Daily Doses (DDDs), Chinese Yuan (CNY), and Defined Daily Drug cost (DDDc). Results After “4 + 7” policy intervention, the procurement volume of bid-winning antihypertensive drugs significantly increased (3.12 million DDD, 95 % CI = 2.14 to 4.10, p < 0.001), while the volume of non-winning drugs decreased (-2.33 million DDD, 95 % CI= -2.83 to -1.82, p < 0.01). The use proportion of bid-winning antihypertensive drugs increased from 12.31 to 87.74 % after policy intervention. The overall costs of the seven “4 + 7” List antihypertensive drugs significantly declined (-5.96 million CNY, 95 % CI= -7.87 to -4.04, p < 0.001) after policy intervention, with an absolute reduction of 36.37 million CNY compared with the pre-“4 + 7” period. The DDDc of bid-winning antihypertensive drugs significantly decreased (-1.30 CNY, 95 % CI= -1.43 to -1.18, p < 0.001), while the DDDc of non-winning (0.28 CNY, 95 % CI = 0.11 to 0.46, p < 0.01) and alternative (0.14 CNY, 95 % CI = 0.03 to 0.25, p < 0.05) antihypertensive drugs increased markedly. Conclusions The implementation of “4 + 7” policy promoted the drug use hypertensive patients gradually concentrated on the quality-guaranteed bid-winning drugs, which might be conducive to improve the overall quality level of drug use of Chinese hypertensive patients. Besides, a preliminary positive policy effect of price cut and cost-saving was observed in the antihypertensive drug category. In the future, price monitoring and drug use management regarding policy-related drugs should also be strengthened.

A machine learning-based price state prediction model for agricultural commodities using external factors

In recent times of noticeable climate change the consideration of external factors, such as weather and economic key figures, becomes even more crucial for a proper valuation of derivatives written on agricultural commodities. The occurrence of remarkable price changes as a result of severe changes in these factors motivates the introduction of different price states, each describing different dynamics of the price process. In order to include external factors we propose a two-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second, predictions of future price states are then obtained from short-term predictions of the external factors by means of either K-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte Carlo simulation, which we compare to Sørensen (J Futures Mark 22(5):393–426, 2002). Thereby we obtain a better approximation of the real futures prices by the simulated futures prices regarding the error measures MAE, RMSE and MAPE. From a practical point of view, these simulations can be used to support the assessment of price risks in risk management systems or as decision support regarding trading strategies under different price states.

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and insist on the use of different methods of deep learning to predict and to this reaction of filling the gap, this study employs five versions of LSTM deep learning techniques for the task of five agricultural commodities prices prediction on univariate time series dataset of Rice, Wheat, Gram, Banana, and Groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing all the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE and two paired t-test with hypothesis and confidence level of 95% as a measure of robustness. The study predicted the one month ahead future price for all the five commodities and compared it with actual prices using said LSTM models and obtained promising results.

Machine Learning Model Approaches for Price Prediction in Coffee Market using Linear Regression, XGB, and LSTM Techniques

Investors and other business persons have a desire to know about the future market price because, if the investors know about the future price of a certain commodity or stock it will help them to make appropriate business decisions and they can also get profit out of their investment. There are many previous researches that has been done on stock market predictions but there is no related research that has been done on Ethiopia commodity exchange (ECX). Performing future price prediction with better accuracy and performing comparative analysis between the algorithms for two of Ethiopia commodity exchange (ECX) items which are Coffee and Sesame as the research key objectives. Three different types of prediction algorithms to predict the future price, such as Linear Regression (LR), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM) was utilized. There are limited researches worked on price prediction of ECX items specifically, the idea of the price prediction on different Stock markets like New York stock market Exchange and other commodity market items prediction in order to develop our research in ECX was presented. The study apart from predicting the future price, comparative analysis was implemented between the prediction algorithms that we used based on their performance. Two different datasets from ECX: coffee and sesame were used. The reason for the utilization of these datasets is, the commodity items are the largest export items in Ethiopia which makes them very important for Ethiopian economy, and the different datasets helps us to get the advantage of evaluating the algorithms with different number of datasets, since sesame dataset has 7205 instances and coffee dataset has 1540 instances and both of them has 11 attributes. We build an android application in order two implement our algorithms on mobile applications and see if it is possible to implement the prediction algorithms on mobile platforms and make it easy and accessible to users. We call this mobile application Ethiopia Coffee Prices Predictor (ECPP). This application will be used to display the prediction result of Ethiopia Coffee price for short period and it is designed in the way to be user friendly. The programming environment used to implement the prediction algorithms is python, java programming language to design our android application and we used PHP to implement the API, and finally we used MySQL database in order to store information’s online and make them accessible everywhere.

Related-Party Transactions and Stock Price Crash Risk: Evidence from China

This paper investigates the association between related-party transactions (RPTs) and stock price crash risk in China. Our investigation is motivated by the controversy in the RPT literature over whether RPTs are value enhancing or opportunistic. Through the lens of stock price crash risk, we reveal that RPTs may violate the arm’s-length assumption of regular market-based transactions, impairing the representational faithfulness and verifiability of accounting data and, consequently, increasing the risk of future price crash. Importantly, we find that this detrimental economic consequence of RPTs is driven by abnormal RPTs that are opportunistic in nature. Our analyses also extend to operating RPTs, related-party loans, and two types of opportunistic RPTs: tunneling and propping. The positive association between RPTs and stock price crash risk is not mediated by financial reporting quality, suggesting that the risk factors associated with RPTs are operational. Our main results remain robust to a series of tests done to address the potential endogeneity between RPTs and stock price crash risk.

Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain

The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. This article considers the application of probabilistic electricity price forecasting models to the wholesale market of Great Britain (GB) and compares them to better understand their capabilities and limits. One of the models that this paper considers is a recent novel X-model that predicts the full supply and demand curves from the bid-stack. The advantage of this model is that it better captures price spikes in the data. In this paper, we provide an adjustment to the model to handle data from GB. In addition to this, we then consider and compare two time-series approaches and a simple benchmark. We compare both point forecasts and probabilistic forecasts on real wholesale price data from GB and consider both point and probabilistic measures.