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Related Concept Videos

System of Memory01:23

System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Chunking and Rehearsal in Sensory Memory01:22

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Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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Long-Term Memory01:18

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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
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Working Memory01:24

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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Two robust long short-term memory frameworks for trading stocks.

Dušan Fister1, Matjaž Perc2,3,4, Timotej Jagrič1

  • 1Faculty of Economics and Business, University of Maribor, Razlagova 14, 2000 Maribor, Slovenia.

Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
PubMed
Summary
This summary is machine-generated.

This study explores advanced trading strategies for stocks with infrequent news. Long short-term memory networks significantly outperform traditional methods, but neither universal nor specific network designs show a clear advantage.

Keywords:
Algorithmic tradingLong short-term memoryMechanical trading systemPortfolio of stocks

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Area of Science:

  • * Computational Finance
  • * Machine Learning Applications
  • * Algorithmic Trading

Background:

  • * Traditional trading strategies struggle with low-information-frequency stock portfolios.
  • * Need for advanced methods to improve daily stock trading performance.
  • * Existing models may not capture complex trading dynamics effectively.

Purpose of the Study:

  • * To identify a superior daily trading strategy for stocks with limited new information.
  • * To compare the efficacy of traditional strategies against novel deep learning approaches.
  • * To evaluate the impact of universal versus stock-specific factors in trading network design.

Main Methods:

  • * Development and testing of traditional trading strategies.
  • * Implementation of long short-term memory (LSTM) networks incorporating general (universal) trading patterns.
  • * Implementation of LSTM networks incorporating specific (stock-specific) trading patterns.

Main Results:

  • * Both universal and stock-specific LSTM networks significantly outperformed traditional trading strategies.
  • * No statistically significant difference in performance was observed between universal and stock-specific LSTM networks.
  • * LSTM networks demonstrate superior adaptability and predictive power in low-information environments.

Conclusions:

  • * Deep learning models, specifically LSTM networks, offer a significant advancement over traditional methods for daily stock trading.
  • * The choice between universal and stock-specific network designs for LSTMs in trading remains an open question.
  • * Further research is needed to determine optimal LSTM architecture for diverse trading scenarios.