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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Confidence Coefficient01:24

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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A hype-adjusted probability measure for NLP stock return forecasting.

Zheng Cao1, Helyette Geman1

  • 1Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States.

Frontiers in Artificial Intelligence
|March 6, 2025
PubMed
Summary

This study introduces a Hype-Adjusted Probability Measure for natural language processing (NLP) stock forecasting. This novel approach enhances prediction accuracy by correcting for news sentiment bias and shifts.

Keywords:
hype-adjusted probability measuremarket volatility forecastnatural language processingsemiconductor industrysentiment analysis

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Area of Science:

  • Quantitative Finance
  • Natural Language Processing (NLP)
  • Computational Finance

Background:

  • Traditional financial forecasting models often struggle with the dynamic nature of news sentiment.
  • Existing Natural Language Processing (NLP) techniques may not adequately capture the nuances of market sentiment.
  • The finance of Asset Pricing offers tools like change of Probability Measure that have not been fully integrated into NLP forecasting.

Purpose of the Study:

  • To introduce a novel Hype-Adjusted Probability Measure for improved stock return and volatility forecasting.
  • To develop a new sentiment score equation that accounts for intraday news impact.
  • To extend the application of Probability Measure concepts from Asset Pricing to NLP-driven financial forecasting.

Main Methods:

  • A new sentiment score equation was developed to quantify the impact of intraday news.
  • The Hype-Adjusted Probability Measure was constructed by redistributing probability weights.
  • The approach addresses news bias, memory, weight, and sentiment direction shifts.
  • Forecasting was applied to U.S. semiconductor stocks.

Main Results:

  • The proposed Hype-Adjusted Probability Measure improves forecast accuracy for stock returns and volatility.
  • The novel sentiment score effectively captures the influence of intraday news.
  • The method demonstrates a successful extension of financial probability measures into NLP forecasting.

Conclusions:

  • The Hype-Adjusted Probability Measure offers a significant advancement in NLP-based financial forecasting.
  • This approach provides a more robust method for incorporating news sentiment into predictive models.
  • The study highlights the potential of integrating advanced financial mathematical tools with NLP for market analysis.