Would AI Stocks Estimate Be as Surprised to USDA Stocks Reports As Private Market Analysts?

The USDA survey-based Quarterly Agriculture Stocks (QAS) reports are the primary source of information regarding the relative supply of U.S. corn, soybeans, and wheat for the last fifty years. Research has examined USDA stock reports and their relevancy to the market (e.g., Isengildina-Massa et al., 2021). In addition, private industry analysts estimate expected quarterly grain stock reports before USDA releases them. Market information firms such as Bloomberg and Reuters publish a subset of these estimates a few days before the USDA reports. Previous research has found that when industry analysts have significant differences in stock expectations compared to what the USDA releases for grain stocks, market prices adjust rapidly to what the USDA found in their survey. Many media outlets and previous research attribute the differences in expectations and changes in market prices to a market surprise (e.g., Karali et al. (2020)). Market analysts, USDA officials, and researchers have offered four reasons for market surprises in the grain stocks reports. First, USDA surveys may need to account for grain in transit when surveying stocks. Second, the market often uses weight (e.g., 60 lbs per bushel) to determine supply, while survey estimates ask how much volume (e.g., bushels) is on the farm or in commercial storage. When there is a deviation in the average weight of a commodity for a season, there could be discrepancies between surveyed stocks and actual stocks by weight. Third, errors in estimating what portion of existing stocks are from old or new crop production may cause surprises in the final annual report before a change in the marketing year. For example, USDA asks in their survey how much old crop corn is on hand on September 1st, although some crops taken in by grain wholesalers can be new crops by this date. There can be discrepancies when the survey respondent must accurately segregate the new and old crop amounts. Fourth, USDA survey-based stock reports contain survey noise. Market analysts may need to account for survey noise in sequential estimates. This paper seeks to use AI methods and large datasets on grain movement to understand the primary reason market analysts are frequently surprised by USDA QAS reports. Given the recent surge in grain movement data, available grain quality data, and data on the output of significant demand sources of grain, particularly at a state level, it is possible to use advances in analyzing high dimensional data (e.g., random forest, gradient boosting) to develop an objective artificial intelligent (AI) market analyst. This paper aims to explore additional public data sources related to commodity demand and supply in the corn, wheat, and soybean markets and apply AI techniques to determine whether data analytics improves the prediction of QAS reports released by USDA for corn, soybeans, and wheat compared to market analysts estimates. Our primary research objective is to determine if AI can more accurately predict QAS estimates from USDA than the survey of Market analysts that Bloomberg and Reuters have historically provided. Our secondary objective is to decompose the surprise by the source of the surprise. In this effort, we use the Extreme Gradient Boosting ML model to predict the stock estimate of the three major commodities (Corn, Soybean, and Wheat). We used grain stocks and production by state, carry-over stock from the previous year, weekly grain loaded on trains and barges, weekly ethanol production, monthly ethanol crushed, and weekly accumulated exports, market analysts\u27 estimates from Bloomberg and Reuters from the year 2007 to the 4th quarter of 2022. We aggregated all these features every quarter to understand the estimate of stock. After accumulating all the features, we cross-checked the values with the national report of these particular years we found consistency among them. This means the features show actual values from each quarter to understand the accurate estimate of the stock. We also grouped each feature according to 10 Agricultural Regions. We found through our machine learning algorithm that production is the most important one to estimate the quarterly stock, with carry-over and accumulated exports in 2nd and 3rd most essential features of the model. We also found that ethanol production and grain exports have an inverse relation with the grain stock every quarter

Risky Ship Breaking Practice in Bangladesh: An Analysis on Present Socio-economic Status of Victim Workers

Shipbreaking is the recycling mechanism that is reprocessed or scrapped or disposed of almost obsolete cargo vessels As the breakdown process of these vessels takes place within a multifarious framework the workforce is confronting many environmental and health barriers throughout the recycling sector Bangladesh is well known for earning a good deal of profit from such a precarious and caustic industry on the South-eastern offshore in the country but on the flip side the masters of this insecure business are actively contravening the employees on the top of that human rights here are completely missing The current situation in the Chittagong ship-breaking area is getting worse day by day while current workers are deprived of basic rights victim workers are completely ignored The concentration of this research paper is to evaluate the socio-economic status of victims workers and to explore the system of rehabilitation of victims workers in shipbreaking divisions This study found that after the fatal accidents the condition of victim workers had become more miserable due to deprivation of compensation and other rehabilitation support from the authorities and the governmen

Session 7: \u3cem\u3eWould AI Stocks Estimate Be as Surprised to USDA Stock Reports as Private Market Analysts?\u3c/em\u3e

Would AI Stocks Estimate Be as Surprised to USDA Stock Reports as Private Market Analysts? Keywords: Machine Learning, Random Forest, Agricultural Commodities Market, Informational Impact, Efficient Market Hypothesis. The USDA survey-based Quarterly Grain Stocks reports are the primary source of information regarding the relative supply of U.S. corn, soybeans, and wheat for the last fifty years. Previous research has examined the accuracy of the USDA stock reports and their relevancy to the market, given alternative sources of estimates (e.g., Isengildina-Massa et al., 2021). For example, private industry analysts also estimate expected quarterly grain stock reports before USDA releases their reports. Market information firms such as Bloomberg and Reuters publish a subset of these estimates a few days before the USDA reports. Previous research has found that when industry analysts have significant differences in stock expectations compared to what the USDA releases for grain stocks, market prices tend to adjust rapidly to what the USDA found in their survey. Many media outlets and previous research attribute the differences in expectations and changes in market prices to a market surprise. Karali et al. (2020) found compelling evidence that the discrepancy in USDA reports from private analysts\u27 expectations plays a vital role in explaining grain futures price movements on report days. Market analysts, USDA officials, and researchers have given four reasons for market surprises in the grain stocks reports. First, USDA surveys may fail to account for grain in transit when surveying stocks. Second, many private analysts use standard conversion rates (e.g., average test weight per bushel of reported corn) for products derived from grain inputs to estimate their expected grain stocks after a quarter. However, these conversion rates may vary because of the quality of the grain and be less (more) than what private analysts estimate. Third, errors in estimating what portion of existing stocks is from old or new crop production may cause surprises in the final annual report before a change in the marketing year. For example, USDA asks in their survey how much old crop corn is on hand on September 1st, although some crops taken in by grain wholesalers can be new crops by this date. Fourth, USDA survey-based stock reports contain survey noise. It is still being determined whether market analysts can correctly consider survey noise when reconciling their estimates versus the USDA and smooth future estimates, assuming some portion of the previous report was due to noise and survey error. What is the primary reason market analysts are frequently surprised by USDA QAS reports? Given the recent surge in grain movement data, available grain quality data, and data on the output of significant demand sources of grain, particularly at a state level, it is possible to use advances in analyzing high dimensional data (e.g., random forest, gradient boosting) to develop an objective artificial intelligent (AI) market analyst. This paper aims to explore additional public data sources related to commodity demand and supply in the corn, wheat, and soybean markets and apply AI techniques to determine whether data analytics improves the prediction of QAS reports released by USDA for corn, soybeans, and wheat. Our primary research objective is to determine if AI can more accurately predict QAS estimates from USDA than the survey of Market analysts that Bloomberg and Reuters have historically provided. Our secondary objective is to attempt to decompose the surprise into by source of surprise. We will use Random Forest ML model to predict the stock estimate of the three major commodities (Corn, Soybean, and Wheat) with all the publicly available data before the national announcement of the Quarterly Stock Report. We will compare the stock estimate provided by our AI techniques to private market analysts, which have been a critical component of information before the announcement days. Our research findings will also decompose the variables most important for explaining market surprises. Specifically, does the amount of grain in transit, changes in demand due to grain quality, or the mixing of new crops and old crops in stock estimates mainly explain the surprise? Further, our findings may determine if private analysts have problems reconciling noise in previous USDA surveys when making future estimates for future reports

HIV/AIDS, health and wellbeing study among International Transport Workers’ Federation (ITF) seafarer affiliates

Background: Transport workers generally face a higher-than-average risk of HIV as well as other health challenges. In order to improve understanding of health issues in the maritime sector, including but not limited to HIV/AIDS, and to prepare appropriate responses the International Transport Workers’ Federation (ITF) conducted a study of the views and needs of those affiliates. Materials and methods: The ITF carried out two surveys. The first consisted of a questionnaire sent to all ITF seafarer affiliates to establish their concerns about health issues, including the impact of HIV/AIDS, and to assess the extent and nature of existing trade union programmes. The second consisted of a knowledge, attitude and behaviour survey on health, wellbeing and AIDS among a cross-section of individual members administered through anonymous and confidential questionnaires by maritime affiliates in four countries in different regions and an identical online questionnaire through Survey Monkey. Results: For the first survey, replies were received from 35 unions in 30 countries, including major seafarer supplying countries — India, Indonesia, Myanmar, Philippines, Turkey, Ukraine — and major beneficial ownership countries such as Germany, Italy, Norway, and South Korea. Health issues of concern included HIV and other sexually transmitted infections for over three-quarters of them, and then alcohol use, weight control, and mental health. All said they would welcome ITF support in starting or strengthening a programme on general health and/or HIV. Replies were received to the second survey from 615 individual seafarers. Half to three-quarters said they worried about their weight, lack of exercise and drinking; over half felt depressed sometimes or often. There were serious knowledge gaps in a number of areas, especially HIV transmission and prevention, as well as high levels of stigma towards workmates with HIV. Conclusions: A number of health issues and information gaps remain unaddressed on board and pre-departure. Mental health is especially neglected but the needs emerge clearly. Seafarers believe that companies should provide programmes but also look to their unions for health information and services. The ITF has an important role to play in supporting affiliated unions in developing activities and in providing technical and strategic guidance.