Econometric Modeling: Capital Markets - Forecasting eJournal最新文献_第5页

Forecasting Value at Risk and Expected Shortfall with Mixed Data Sampling 混合数据抽样预测风险值和预期不足

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-12-25 DOI: 10.2139/ssrn.3509425

Trung H. Le

引用次数: 22

ЦЕНЫ НА НЕФТЬ И СОЛНЕЧНАЯ АКТИВНОСТЬ: ДОКАЗАТЕЛЬСТВО СИЛЬНЫХ СВЯЗЕЙ (1861-2019 ГГ.) (Oil Prices and Solar Activity: Evidence of Strong Ties (1861-2019))

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-12-23 DOI: 10.2139/ssrn.3542429

V. Belkin

{"title":"ЦЕНЫ НА НЕФТЬ И СОЛНЕЧНАЯ АКТИВНОСТЬ: ДОКАЗАТЕЛЬСТВО СИЛЬНЫХ СВЯЗЕЙ (1861-2019 ГГ.) (Oil Prices and Solar Activity: Evidence of Strong Ties (1861-2019))","authors":"V. Belkin","doi":"10.2139/ssrn.3542429","DOIUrl":"https://doi.org/10.2139/ssrn.3542429","url":null,"abstract":"Russian Abstract: В статье используется методологический подход, основанный У. С. Джевонсом и А. Л. Чижевским. Годы солнечных циклов были пронумерованы по установленному в астрофизике порядке, сгруппированы и сопоставлены со средними арифметическими значениями цен на нефть сорта «Брент». Группировка статистических данных по всем годам циклов солнечной активности позволила построить функцию цен на сырую нефть сорта «Брент» (зависимой переменной) и номеров по порядку лет цикла солнечной активности (независимой переменной) с коэффициентом достоверности аппроксимации, равным 0,9897. Данная функция позволяет прогнозировать цены на нефть сорта «Брент» следующих лет на основе порядкового номера года в текущем цикле солнечной активности. Из неё следует, что в 2020 г. должно произойти снижение цен на нефть до уровня 48,798 дол. за баррель. Прогнозное значение цены нефти для 2021 года равняется 47,957, а для 2022 года – 61,175 дол. за баррель. English Abstract: In this work author is using methodological approach developed by W.S. Jevons and A.L Chizhevsky. Years of solar cycles were numbered in prescribed astrophysics manner, combined and compared against average values of Crude Brent price. Grouping of data by years of each solar cycle allowed developing regressive polynomial model where Crude Brent price is dependent variable and number of respective year in the solar cycle is independent. Resulting R-squared value is equal to 0.9897. This model allows forecasting of prices on Brent Crude basing solely on year number in current solar cycle. Model suggests that 2020’s Brent Crude price will decrease to USD 48.798 level per barrel and forecasted values of Crude Brent for 2021 and 2022 are USD 47.957 and USD 61.175 per barrel respectively.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75497648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Benchmarked Us State Employment Data in Realtime 实时预测基准美国州就业数据

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-12-02 DOI: 10.21033/wp-2019-11

Scott A. Brave, Charles S. Gascon, W. Kluender, Thomas Walstrum

引用次数: 1

A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing 基于深度学习和自然语言处理的稳健股票价格预测模型

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-12-01 DOI: 10.2139/ssrn.3502624

Sidra Mehtab, Jaydip Sen

{"title":"A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing","authors":"Sidra Mehtab, Jaydip Sen","doi":"10.2139/ssrn.3502624","DOIUrl":"https://doi.org/10.2139/ssrn.3502624","url":null,"abstract":"Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86444354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 72

Forecasting with a Panel Tobit Model 面板Tobit模型预测

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-12-01 DOI: 10.2139/ssrn.3502279

L. Liu, H. Moon, F. Schorfheide

引用次数: 16

The Conditional Expected Market Return 有条件预期市场回报

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-11-18 DOI: 10.2139/SSRN.3033936

Fousseni Chabi-Yo, Johnathan Loudis

引用次数: 41

CUK Converter Based Reduction of Commutation Ripples in BLDCM 基于CUK变换器的无刷直流电机换相波纹抑制

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-10-31 DOI: 10.34218/ijeet.10.5.2019.003

R. Sivakami, G. Sugumar

{"title":"CUK Converter Based Reduction of Commutation Ripples in BLDCM","authors":"R. Sivakami, G. Sugumar","doi":"10.34218/ijeet.10.5.2019.003","DOIUrl":"https://doi.org/10.34218/ijeet.10.5.2019.003","url":null,"abstract":"In view of Cuk converter, a novel compensation torque swell decrease technique is proposed for brush-less DC engine (BLDCM) in this paper with renewable energies as source. Yield modes (buck-help mode and lift mode) of the Cuk converter during recompense period and typical conduction period are modified by structuring a mode choice circuit, which can lessen compensation torque swell over the whole speed go. During the compensation time frame, Cuk converter works in the lift mode to step up the information voltage of three-stage connect inverter and afterward satisfy the voltage need of substitution period, with the end goal that the replacement torque swell can be diminished by keeping the non-commutated current unfaltering. So as to improve the usage pace of the converter, during the ordinary conduction time frame, Cuk converter works in the buck-help mode and the information voltage of three-stage connect inverter is directed by receiving PAM (Pulse Amplitude Modulation) technique without the inverter PWM slashing, which can lessen the voltage spike harm to the engine windings brought about by turn-on/off of MOSFET in the inverter and rearrange the program of regulation strategy further with renewable energy as its main source. The trial results confirm the accuracy of the hypothesis and the viability of the proposed methodology.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"2013 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83161748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Do Any Economists Have Superior Forecasting Skills? 经济学家是否有更强的预测能力?

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-10-30 DOI: 10.2139/ssrn.3479463

Ritong Qu, A. Timmermann, Yinchu Zhu

引用次数: 4

Calculators and Quacks: Feeling the Economy's Pulse in Times of Crisis 计算器和庸医:在危机时期感受经济脉搏

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-10-29 DOI: 10.2139/ssrn.3477291

H. Maas

{"title":"Calculators and Quacks: Feeling the Economy's Pulse in Times of Crisis","authors":"H. Maas","doi":"10.2139/ssrn.3477291","DOIUrl":"https://doi.org/10.2139/ssrn.3477291","url":null,"abstract":"In this paper, I take a talk show in which Coen Teulings, then Director of the official Dutch Bureau for Economic Forecasting and Policy Analysis (CPB) was interviewed about its economic forecasts in the immediate aftermath of the financial crisis of 2008 as point of entry into an examination into how personal experience and judgment enter, and are essential for, the production and presentation of economic forecasts. During the interview it transpired that CPB did not rely on its macroeconomic models, but on personal experience encapsulated in “hand-made” monitors, to observe the unfolding crisis; monitors that were, in Teulings’ words, used to “feel the pulse” of the Dutch economy. I will take this metaphor as a cue to present several historical episodes in which models, numbers, and a certain feel for economic phenomena aimed to make CPB economists’ research more precise. These episodes are linked with a story about vain attempts by CPB director Teulings to drive out the personal from economic forecasting. The crisis forced him to recognize that personal experience was more important in increasing the precision of economic forecasts than theoretical deepening. The crisis thus both challenged the belief in the supremacy of theory driven, computer-based forecasting, and helped foster the view that precision is inevitably linked to judgment, experience and observation, and not seated in increased attention to high theory; scientifically sound knowledge proved less useful than the technically unqualified experiential knowledge of quacks.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90209859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When and Why Do Stock and Bond Markets Predict Economic Growth? 股票和债券市场何时以及为什么预测经济增长?

Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-10-23 DOI: 10.2139/ssrn.3474420

D. McMillan

{"title":"When and Why Do Stock and Bond Markets Predict Economic Growth?","authors":"D. McMillan","doi":"10.2139/ssrn.3474420","DOIUrl":"https://doi.org/10.2139/ssrn.3474420","url":null,"abstract":"We consider whether key financial variables predict macroeconomic series and if any predictive power for output growth is also seen in consumption or investment growth. Such information will allow the use of financial markets as a leading indicator for macroeconomic performance. Full sample results suggest that aggregate stock returns and the 10-year minus 3-month term structure exhibit a positive and significant predictive effect on subsequent output, consumption and investment growth. Additionally, the change in the 3-month Treasury bill has predictive power for output and investment growth. Sub-sample analysis reveals that while the term structure exhibits relatively constant predictive power that arising from stock returns largely only occurs during the great moderation period, whereas for the change in the short-term rate it largely arises in the period following the financial crisis. Results also reveal similarity in the predictive relations for output growth and investment growth but less so for consumption growth. We extend the analysis to include commodity, housing and the corporate bond markets. Full sample results reveal limited additional predictive ability, while the REIT returns do provide positive predictive power for output and investment growth over a one-quarter horizon, with the default return doing likewise at the four-quarter horizon. Notably, sub-sample results reveal a change in the sign of the predictive coefficient around the dotcom bubble and crash period.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89121989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1