Buy, sell, or hold? A sense-making account of factors influencing trading decisions

Sobolev, Daphne; Chan, Bryan; Harvey, Nigel

doi:10.1080/23322039.2017.1295618

Cited by 3 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It acts as a conduit for transmitting information, both anticipated and unexpected, to market participants (Cheng, 2014). This information is then assimilated into asset prices, influencing trading decisions and market sentiment (Hanaki et al, 2018;Sobolev et al, 2017). Historically, significant market movements can often be traced back to the release of major news events, be it economic indicators, corporate earnings reports, or geopolitical developments (Goodell & Goutte, 2021;Jalal et al, 2020).…”

Section: The Role Of News In Financial Marketsmentioning

confidence: 99%

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

A.,

Prakash,

et al. 2023

CBR

View full text Add to dashboard Cite

This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.

show abstract

Section: The Role Of News In Financial Marketsmentioning

confidence: 99%

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

A.,

Prakash,

et al. 2023

CBR

View full text Add to dashboard Cite

show abstract

“…Metode analisis data dalam penelitian ini yaitu analisis regresi non-linear dan analisis beda dua grup. Analisis regresi non-linear digunakan untuk menunjukkan fenomena loss aversion yang dipostulatkan dalam teori prospek melalui fungsi nilai S-shape (Lewandowski 2017 Keputusan investasi saham didasarkan pada transaksi atau perdagangan saham berupa penjualan, pembelian, atau menahan saham (Sobolev et al 2017). Pada kondisi pasar yang berbeda yaitu kondisi rugi dan untung, investor akan mengalami ketidakpastian dalam menentukan keputusannya karena bergantung pada risiko yang dihasilkan dari masing-masing kondisi.…”

Section: Metode Penelitianunclassified

Analisis Penghindaran Risiko Pada Keputusan Investasi Di Pasar Modal Indonesia

Sakinah¹,

Purwanto²,

Ermawati³

2021

jabm

View full text Add to dashboard Cite

Decision making in the capital market is not always rational. It is due to the emotional involvement of investors, which is described through financial behavior. Investor decision making based on financial behavior is considered as a loss aversion contained in prospect theory. In this theory, the prospect of risk aversion behavior when the market is profitable, but this behavior becomes an obstacle for investors to get higher returns. This study aimed to analyze the risk avoidance behavior that limits investment decision making in Indonesian stock investors. Primary data were obtained from individual investor transactions on the IDX, and secondary data were in the form of daily stock data. Identification of risk aversion was carried out using a non-linear regression method. The measurement of psychological resistance was carried out through an independent sample t-test by measuring the difference between actual and expected returns. The results showed that Indonesia's capital market investors have a riskaverse behavior in the gain domain and risk-seeking in the loss domain. Risk-averse behavior could reduce investment productivity.

show abstract

“…Even though several studies [14][15][16] were presented regarding stock market prediction, most of them were focused on future price prediction only. Such studies do not contribute much to assist investors and buyers in their decision-making since the main focus of investors is on the reversal points of stocks instead of each changing price.…”

Section: Introductionmentioning

confidence: 99%

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

et al. 2022

View full text Add to dashboard Cite

Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to predict future stock trends based on historical financial data, social media news, financial news, and stock technical indicators (STIs). This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated STIs were first used as the input for the autoencoder for dimensionality reduction, resulting in less correlation between the STIs. These STIs, along with the Yahoo finance data, were then fed into the 1D DenseNet. The resultant features obtained from the 1D DenseNet were then used as input for the softmax layer residing inside the 1D DenseNet framework for the prediction of closing stock prices for short-, medium-, and long-term perspectives. Based on the predicted trends of the stock prices, our model presented the user with one of three suggested signals, i.e., buy, sell, or hold. The experimental results showed that the proposed approach outperformed the state-of-the-art techniques by obtaining a minimum MAPE value of 0.41.

show abstract

Buy, sell, or hold? A sense-making account of factors influencing trading decisions

Cited by 3 publications

References 40 publications

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

Analisis Penghindaran Risiko Pada Keputusan Investasi Di Pasar Modal Indonesia

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

Contact Info

Product

Resources

About