Dual-Class Stocks: Can They Serve as Effective Predictors?

Veli Safak

Article Details

Dual-Class Stocks: Can They Serve as Effective Predictors?

Authors:

Veli Safak

Publisher:

Izmir Katip Celebi University

Main Topics:

Makine Öğrenme (Diğer) | Veri Madenciliği ve Bilgi Keşfi

Journal:

Journal of Artificial Intelligence and Data Science Vol.4 Issue 1, pp.44-58

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001320137

pISSN:

2791-8335

Keywords:

Abstract:

 Kardemir Karabuk Iron Steel Industry Trade  Co. Inc. is the 24th largest industrial company in Turkey with three stocks listed in the Borsa Istanbul: KRDMA, KRDMB, and KRDMD. While the only difference be-tween these three stocks is about voting power, prices of these stocks have exhibited significant divergence for a considerable period. In this paper, I examine the divergence patterns between these three stock prices between Jan-2001 and Jul-2023. There is no evidence supporting the efficiency of dual-class stocks as predictors of each other despite a strong coherence between them. Finally, I propose a novel training set selection rule for LSTM models incorporating a rolling training set and demonstrate its significant superiority in predicting future stock prices compared to conventional use of LSTM models employing large training sets.

 Kardemir Karabuk Iron Steel Industry Trade  Co. Inc. is the 24th largest industrial company in Turkey with three stocks listed in the Borsa Istanbul: KRDMA, KRDMB, and KRDMD. While the only difference be-tween these three stocks is about voting power, prices of these stocks have exhibited significant divergence for a considerable period. In this paper, I examine the divergence patterns between these three stock prices between Jan-2001 and Jul-2023. There is no evidence supporting the efficiency of dual-class stocks as predictors of each other despite a strong coherence between them. Finally, I propose a novel training set selection rule for LSTM models incorporating a rolling training set and demonstrate its significant superiority in predicting future stock prices compared to conventional use of LSTM models employing large training sets.

 Kardemir Karabuk Iron Steel Industry Trade  Co. Inc. is the 24th largest industrial company in Turkey with three stocks listed in the Borsa Istanbul: KRDMA, KRDMB, and KRDMD. While the only difference be-tween these three stocks is about voting power, prices of these stocks have exhibited significant divergence for a considerable period. In this paper, I examine the divergence patterns between these three stock prices between Jan-2001 and Jul-2023. There is no evidence supporting the efficiency of dual-class stocks as predictors of each other despite a strong coherence between them. Finally, I propose a novel training set selection rule for LSTM models incorporating a rolling training set and demonstrate its significant superiority in predicting future stock prices compared to conventional use of LSTM models employing large training sets.

 Kardemir Karabuk Iron Steel Industry Trade  Co. Inc. is the 24th largest industrial company in Turkey with three stocks listed in the Borsa Istanbul: KRDMA, KRDMB, and KRDMD. While the only difference be-tween these three stocks is about voting power, prices of these stocks have exhibited significant divergence for a considerable period. In this paper, I examine the divergence patterns between these three stock prices between Jan-2001 and Jul-2023. There is no evidence supporting the efficiency of dual-class stocks as predictors of each other despite a strong coherence between them. Finally, I propose a novel training set selection rule for LSTM models incorporating a rolling training set and demonstrate its significant superiority in predicting future stock prices compared to conventional use of LSTM models employing large training sets.

Article Details

Dual-Class Stocks: Can They Serve as Effective Predictors?

Authors:

Veli Safak

Publisher:

Izmir Katip Celebi University

Main Topics:

Makine Öğrenme (Diğer) | Veri Madenciliği ve Bilgi Keşfi

Journal:

Journal of Artificial Intelligence and Data Science Vol.4 Issue 1, pp.44-58

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001320137

pISSN:

2791-8335

Dual-class stock

long short-term memory

stock price prediction

wavelet analysis

Issues

Article Details

Dual-Class Stocks: Can They Serve as Effective Predictors?

Authors: Veli Safak

Publisher: Izmir Katip Celebi University

Main Topics: Makine Öğrenme (Diğer) | Veri Madenciliği ve Bilgi Keşfi

Journal: Journal of Artificial Intelligence and Data Science Vol.4 Issue 1, pp.44-58

Document Type: Article

Publication Date: 6/2024

Document Language: English

Document ID (Org No.): 2001320137

pISSN: 2791-8335

Dual-class stock

long short-term memory

stock price prediction

wavelet analysis

Issues

Authors:

Veli Safak

Publisher:

Izmir Katip Celebi University

Main Topics:

Makine Öğrenme (Diğer) | Veri Madenciliği ve Bilgi Keşfi

Journal:

Journal of Artificial Intelligence and Data Science Vol.4 Issue 1, pp.44-58

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001320137

pISSN:

2791-8335