Tasirin ChatGPT akan Kadarorin Crypto masu alaƙa da AI: Shaida daga Binciken Sarrafa Kama-da-Kama

Teburin Abubuwan Ciki

1 Gabatarwa

Ƙaddamar da OpenAI's ChatGPT a ranar 30 ga Nuwamba, 2022, ta wakilci wani lokaci mai canzawa a ci gaban haɓakar hankali. Wannan babban samfurin harshe na musamman wanda ya nuna iyawar sarrafa harshe na halitta wanda ba a taɓa ganin irinsa ba, ya cimma manyan nasarori ciki har da wucewa jarrabawar ƙwararru da kuma kaiwa fiye da miliyan 100 masu amfani a cikin watanni biyu—mafi saurin girma na masu amfani a tarihi.

Fasahar ƙwararrun ta haifar da ci gaban kasuwanci na AI kuma ta haɓaka ƙoƙarin canzawa zuwa na dijital a duk sassa. Rahoton kafofin watsa labarai ya nuna yuwuwar haɗawa cikin manyan injunan bincike, wanda ya sa manyan kamfanoni na fasaha kamar Google da Baidu suka mayar da martani. Waɗannan abubuwan sun nuna haɓakar ƙimar fasahar AI a tsakanin masu saka hannun jari, musamman ma suna shafar kadarorin crypto masu alaƙa da AI waɗanda ba su da alaƙa kai tsaye da ChatGPT.

2 Hanyar Bincike

2.1 Sarrafa Kama-da-Kama

Binciken ya yi amfani da hanyar sarrafa bambanci-bambanci don ware tasirin ƙaddamarwar ChatGPT akan ribar cryptocurrency masu alaƙa da AI. Wannan hanyar ta haɗa abubuwa na hanyoyin sarrafa kama-da-kama tare da kimanta bambanci-bambanci don ƙirƙirar ƙungiyar kulawa mai nauyi wacce ta dace da halayen ƙungiyar da ake magani a baya.

2.2 Tattara Bayanai

An tattara bayanai daga musayar cryptocurrency da yawa don alamun AI masu alaƙa da aka gano ta hanyar takaddun farko, bayanin aikin, da rarrabuwar al'umma. Lokacin samfurin ya ƙunshi watanni shida kafin da bayan ƙaddamarwar ChatGPT, tare da bayanan farashi na yau da kullun da ƙididdiga na ciniki. Bayanan girman binciken Google don sharuɗɗan AI sun zama wakili na hankalin masu zuba jari.

3 Sakamako

3.1 Tasirin ChatGPT akan Ribar

Binciken ya nuna "tasirin ChatGPT" masu mahimmanci tare da kadarorin crypto masu alaƙa da AI suna samun matsakaicin riba na 10.7% zuwa 15.6% a cikin watanni ɗaya bayan ƙaddamarwa, da kuma 35.5% zuwa 41.3% a cikin watanni biyu. Waɗannan tasirin suna ci gaba bayan sarrafa yanayin cryptocurrency na kasuwa gaba ɗaya da sauran abubuwan da suka haɗa.

3.2 Binciken Girman Binciken Google

Girman binciken Google don sharuɗɗan AI ya fito a matsayin mahimman alamomin farashi bayan ƙaddamarwar ChatGPT. Binciken alaƙa ya nuna kyakkyawar alaƙa tsakanin haɓakar girman bincike da motsin farashi na gaba a cikin kadarorin crypto masu alaƙa da AI, yana nuna hankalin masu zuba jari na cikin gida ya haifar da babban martanin kasuwa.

4 Aiwatar da Fasaha

4.1 Tsarin Lissafi

Mai kimanta bambanci-bambanci na kama-da-kama za a iya tsara shi kamar haka:

$$\hat{\tau}_{SDID} = \frac{1}{T_1} \sum_{t=T_0+1}^{T} \left[ Y_{1t} - \sum_{j=2}^{J+1} \hat{w}_j Y_{jt} \right] - \frac{1}{T_0} \sum_{t=1}^{T_0} \left[ Y_{1t} - \sum_{j=2}^{J+1} \hat{w}_j Y_{jt} \right]$$

inda $Y_{1t}$ yake wakiltar sakamako na naúrar da aka yi wa magani, $Y_{jt}$ na naúrar kulawa, $\hat{w}_j$ nauyin sarrafa kama-da-kama ne, $T_0$ lokacin kafin magani ne, kuma $T_1$ lokacin bayan magani ne.

4.2 Aiwar da Lambar Tsarin

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

def synthetic_did(treatment_series, control_matrix, pre_periods):
    """
    Aiwar da kimanta bambanci-bambanci na kama-da-kama
    """
    # Lissafa nauyin sarrafa kama-da-kama
    X_pre = control_matrix[:pre_periods]
    y_pre = treatment_series[:pre_periods]
    
    model = LinearRegression(fit_intercept=False, positive=True)
    model.fit(X_pre.T, y_pre)
    weights = model.coef_
    
    # Lissafa jerin sarrafa kama-da-kama
    synthetic_control = weights @ control_matrix
    
    # Lissafa tasirin magani
    post_periods = len(treatment_series) - pre_periods
    treatment_effect = (treatment_series[pre_periods:].mean() - 
                       synthetic_control[pre_periods:].mean())
    
    return treatment_effect, weights, synthetic_control

5 Bincike na Asali

Binciken da Saggu da Ante (2023) suka yi ya ba da shaida mai ƙarfi game da tasirin ɓarkewar fasaha a kasuwannin cryptocurrency, yana nuna yadda ci gaban AI na ƙwararrun zai iya haifar da ƙimar ƙima a duk kadarorin dijital masu alaƙa. Binciken ya yi daidai da ka'idar farashi ta hankali da Barber da Odean (2008) suka gabatar, inda masu zuba jari na cikin gida suka sayi hannun jari masu jan hankali da yawa. A cikin yanayin kadarorin AI crypto, ChatGPT ya zama babban girgiza hankali wanda ya mayar da jarin masu zuba jari zuwa ga babban tsarin AI.

Ta hanyar bincike, binciken ya ci gaba da binciken cryptocurrency ta hanyar amfani da dabarun bambanci-bambanci na kama-da-kama, yana ginawa akan tsarin sarrafa kama-da-kama da Abadie et al. (2010) suka haɓaka. Wannan hanyar tana magance ƙalubale na asali a cikin binciken abubuwan da suka faru na cryptocurrency inda keɓantattun halayen kadarorin crypto suka sa a gina ƙungiyoyin kulawa na al'ada. Hanyar tana da kamanceceniya da hanyoyin da ake amfani da su wajen nazarin tasirin amfani da fasaha a cikin kuɗin al'ada, kamar tasirin dandamalin ciniki ta wayar hannu akan shigar kasuwa da Shiller (2015) ya rubuta.

Girman tasirin da aka lura—daga 35.5% zuwa 41.3% cikin watanni biyu—ya wuce yawan tasirin sanarwar fasaha a kasuwannin al'ada. Wannan haɓaka mai yiwuwa yana nuna musamman hankalin kasuwannin cryptocurrency ga labari da yanayin hankali, kamar yadda Shiller (2017) ya yi hasashe a cikin aikinsa na tattalin arziƙin labari. Sakamakon ya nuna cewa kadarorin crypto masu alaƙa da AI suna aiki a matsayin fare-fare akan ci gaban fasahar AI, wanda ya sa su zama masu saukin kamuwa da ci gaban fasahar AI maƙwabta.

Binciken girman binciken Google ya haɗa binciken da Da et al. (2011) suka yi akan ma'aunin FEARS, yana nuna cewa ma'aunin hankali na tushen bincike yana hasashen motsin farashi na tushen cikin gida a cikin kadarorin hasashe. Dorewar tasirin ChatGPT tsawon watanni biyu yana ƙalubalantar ingantaccen kasuwa a cikin kasuwannin cryptocurrency, daidai da hasashen kasuwa na daidaitawa da Lo (2004) ya gabatar. Wannan yana da muhimman abubuwa ga tsarin ka'idoji da kariyar masu zuba jari a cikin kasuwannin kadarorin dijital masu saurin canzawa.

6 Aikace-aikace na Gaba

Hanyar da binciken suna da aikace-aikace masu mahimmanci da yawa don bincike da aiki na gaba:

• Sa ido kan Kasuwa na Ainihi: Haɓaka tsarin kai tsaye wanda ke bin ci gaban fasaha da yuwuwar tasirin ɓarkewar su akan nau'ikan kadarori masu alaƙa
• Haɓaka Tsarin Ka'idoji: Sanar da yanke shawara game da kariyar masu zuba jari a cikin motsin kasuwa na fasaha
• Haɓaka Dabarun Fayil: Ƙirƙirar dabarun ƙididdiga waɗanda ke ɗaukar tasirin ɓarkewar fasaha bisa tsari
• Binciken Keta-Kadaro: Miƙa hanyar don nazarin alaƙar tsakanin ci gaban fasaha da kayan aikin kuɗi daban-daban
• Haɗin AI: Haɓaka tsarin AI wanda zai iya hasashen tasirin mataki na biyu na nasarorin fasaha

Hanyoyin bincike na gaba sun haɗa da binciken dorewar waɗannan tasirin, nazarin tasiri daban-daban a duk sassan ƙananan sassan AI crypto, da haɓaka tsarin faɗakarwa na farko don motsin kasuwa na hankali.

7 Nassoshi

1. Abadie, A., Diamond, A., & Hainmueller, J. (2010). Hanyoyin sarrafa kama-da-kama don nazarin kwatancen shari'a: Kimanta tasirin shirin sarrafa taba na California. Journal of the American Statistical Association, 105(490), 493-505.
2. Barber, B. M., & Odean, T. (2008). Duk abin da ke kyalkyali: Tasirin hankali da labarai akan halayen sayan masu zuba jari da na hukumomi. The Review of Financial Studies, 21(2), 785-818.
3. Da, Z., Engelberg, J., & Gao, P. (2011). Neman hankali. The Journal of Finance, 66(5), 1461-1499.
4. Lo, A. W. (2004). Hasashen kasuwanni masu daidaitawa: Ingantaccen kasuwa ta mahangar juyin halitta. Journal of Portfolio Management, 30(5), 15-29.
5. Saggu, A., & Ante, L. (2023). Tasirin ChatGPT akan Kadarorin Crypto masu alaƙa da AI: Shaida daga Binciken Sarrafa Kama-da-Kama. Finance Research Letters, 103993.
6. Shiller, R. J. (2015). Farin ciki mara hankali. Princeton university press.
7. Shiller, R. J. (2017). Tattalin arziƙin labari. American Economic Review, 107(4), 967-1004.