External macroeconomic shocks and stock price behavior in Nigeria

This research work investigates the relationship between external macroeconomic shocks and stock price behavior in Nigeria. Variables such as exchange rate (EXR), US real interest rate (USRINTR), and world oil price (WOP) are adopted to capture external macroeconomic shocks while all share price index is used to proxy stock price. The research work uses Johansen cointegration and structural vector autoregressive model as the estimation method. Findings from the study confirm that no long-term co-movement exists between the stock price and the selected external shocks. Findings from the study equally show that both US real interest rate (USRINTR) and world oil price (WOP) are the major external shock predictors of the stock price in Nigeria.

Supply Chain and Logistics of Fish: A Case Study of Jamalpur District Markets in Bangladesh

Fishery sector plays a vital role in the socio-economic development of Bangladesh. In the fish economy of Bangladesh, one of the most flourishing traffic activities is fish marketing. This study was aimed to investigate the fish species availability, supply chain, quality loss, price behavior and constrains associated with fish marketing in order to provide better suggestion for efficient fish marketing in Jamalpur district of Bangladesh. Data were collected form Sadar upazila markets by using a structured interview schedule, focus group discussions (FGD) and key informant interviews (KII) with the stakeholders involved in supplying fish from farm to fork, such as- aratdars, retailers, and consumers during 2020-21. A total 78 fish species were found (72 Fresh water and 2 Marine water), among them 20 frequently, 17 less amount, 25 occasionally, and 16 species were rarely available in the market. It was estimated that 72% fish species were sourced from culture fishery whereas 28% were from capture fishery. About 91.5% fishes in Jamalpur markets were supplied locally, whereas rests were from other districts. The post-harvest quality losses of fish in the sampled markets were assessed which indicate that small indigenous species (SIS) spoiled quicker than medium to large sizes fishes due to different factors. The price behavior of the fish market influenced by the demand of the fish species, quality, size, catching sources (culture fishery or capture fishery) and the purchasing time (beginning, middle or last time of market). The study found positive correlation between the market price and fish quality. Several problems of fish market were identified resulting consumer dissatisfaction and financial losses for fish traders. Considering the importance of fish market to the economy, this study suggested to take necessary steps for establishing modern fish market to ensure the adequate quality maintenance of fish with efficient marketing.

Can we imitate stock price behavior to reinforcement learn option price?

This paper presents a framework of imitating the price behavior of the underlying stock for reinforcement learning option price. We use accessible features of the equities pricing data to construct a non-deterministic Markov decision process for modeling stock price behavior driven by principal investor's decision making. However, low signal-to-noise ratio and instability that appear immanent in equity markets pose challenges to determine the state transition (price change) after executing an action (principal investor's decision) as well as decide an action based on current state (spot price). In order to conquer these challenges, we resort to a Bayesian deep neural network for computing the predictive distribution of the state transition led by an action. Additionally, instead of exploring a state-action relationship to formulate a policy, we seek for an episode based visible-hidden state-action relationship to probabilistically imitate principal investor's successive decision making. Our algorithm then maps imitative principal investor's decisions to simulated stock price paths by a Bayesian deep neural network. Eventually the optimal option price is reinforcement learned through maximizing the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths of the underlying.

Can we imitate stock price behavior to reinforcement learn option price?

This paper presents a framework of imitating the price behavior of the underlying stock for reinforcement learning option price. We use accessible features of the equities pricing data to construct a non-deterministic Markov decision process for modeling stock price behavior driven by principal investor's decision making. However, low signal-to-noise ratio and instability that appear immanent in equity markets pose challenges to determine the state transition (price change) after executing an action (principal investor's decision) as well as decide an action based on current state (spot price). In order to conquer these challenges, we resort to a Bayesian deep neural network for computing the predictive distribution of the state transition led by an action. Additionally, instead of exploring a state-action relationship to formulate a policy, we seek for an episode based visible-hidden state-action relationship to probabilistically imitate principal investor's successive decision making. Our algorithm then maps imitative principal investor's decisions to simulated stock price paths by a Bayesian deep neural network. Eventually the optimal option price is reinforcement learned through maximizing the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths of the underlying.

A theory of sharecropping: the role of price behavior and imperfect competition

A theory of sharecropping: the role of price behavior and imperfect competition

A theory of sharecropping: the role of price behavior and imperfect competition

A theory of sharecropping: the role of price behavior and imperfect competition

Influence of Chinese National Centralized Drug Procurement on the price of policy-related drugs: an interrupted time series analysis

Background: The Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China in 2019. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study aims to examine the impact of "4+7" policy on the price of policy-related drugs.Methods: This study used drug purchasing order data from the Centralized Drug Procurement Survey in Shenzhen 2019, covering 24 months from January 2018 to December 2019. Eighty-two drugs (by generic name) were selected as study samples, including "4+7" policy-related drugs (consisted of winning and non-winning products) and alternative drugs. Single-group Interruption Time Series (ITS) analysis was adopted to examine the change of Drug Price Index (DPI) for policy-related drugs.Results: The ITS analysis showed that the DPI of winning (-0.183 per month, p<0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p<0.0001) after "4+7" policy.Conclusions: These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Influence of Chinese National Centralized Drug Procurement on the Price of Policy-Related Drugs: An Interrupted Time Series Analysis

In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p&amp;lt;0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p&amp;lt;0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Nonlinearities and Chaos: A New Analysis of CEE Stock Markets

After a long transition period, the Central and Eastern European (CEE) capital markets have consolidated their place in the financial systems. However, little is known about the price behavior and efficiency of these markets. In this context, using a battery of tests for nonlinear and chaotic behavior, we look for the presence of nonlinearities and chaos in five CEE stock markets. We document, in general, the presence of nonlinearities and chaos which questions the efficient market hypothesis. However, if all tests highlight a chaotic behavior for the analyzed index returns, there are noteworthy differences between the analyzed stock markets underlined by nonlinearity tests, which question, thus, their level of significance. Moreover, the results of nonlinearity tests partially contrast the previous findings reported in the literature on the same group of stock markets, showing, thus, a change in their recent behavior, compared with the 1990s.